Culture and Methodologies

Every business values experienced and talented professionals. However, it is not always feasible to maintain a large in-house team due to constraints on resources and opportunities. The great thing is there are many ways to partner with developers that align perfectly with your project's needs. Remember, there is no universal team or collaboration method that fits every scenario. Factors like budget, available resources, deadlines, project requirements, size, and tasks all play a role in determining the best way to work together. It is crucial to select the right engagement model to ensure everyone is on the same page and you get the most value for your money. Continue reading to dive into the top six engagement models: their main characteristics, pros, cons, and when to use each one. Wondering which model is the perfect fit for your upcoming project? Discover the answer in my article. 1. Dedicated Team Model The dedicated team engagement model lets you expand your in-house team with experts who fully align with your company's culture, guidelines, and best practices. Team members are chosen based on their skills, technological expertise, and how they fit the project's objectives. Even though the team works remotely, they operate just as if they were right in your office. To make this model work, it is crucial to partner with a trustworthy company. Many development firms, like Brights and ScienceSoft, offer this model today. Key Benefits of the Dedicated Team Model: More cost-effective than many other models. Flexibility in the range of tasks; there is no need for constant coordination with the provider. Ease of adjusting requirements or task priorities as the project progresses. Seamless integration of the dedicated experts into your operations. Tight-knit collaboration between the client and the team. Things to Keep in Mind: Not the best fit for short-duration projects. The client bears most of the project risks. There might be a need for in-house employees to oversee the dedicated developers. When is the Dedicated Team Model the Right Choice? Consider the dedicated team engagement model if you are tackling a long-term project where requirements might evolve or if your large-scale project lacks the requisite in-house expertise. 2. Outstaff Model The outstaff engagement model allows companies to expand their internal teams by hiring remote professionals. Instead of recruiting these experts directly from the job market, you engage them through a third-party provider. Essentially, you are drawing from the outstaffing company's pool, where these experts are already employed. This approach allows companies to effectively "lease" professionals without the strings of formal employment obligations. Many IT firms offer outstaffing today, with companies like Relevant Software and Devox Software leading the charge. Key Benefits of the Outstaff Model: Easily expand your internal team with the needed expertise for short-term or long-term projects. Address gaps in your team's qualifications. Cost benefits arise from savings on administrative and operational costs tied to full-time employment. Opportunity to quickly bring on board niche specialists for tasks that are not recurrent. Things to Keep in Mind: A technical lead might be necessary to oversee these remote professionals. The client is primarily responsible for the project's outcome. There could be communication challenges with remote developers. When is the Outstaff Model the Right Choice? Opt for this engagement model if your organization has sporadic IT needs that might come up seasonally, for specific projects, or intermittently. 3. Staff Augmentation Model The staff augmentation engagement model allows businesses to bolster their in-house teams by adding specific experts to meet certain goals within set timelines. Under this model, an external supplier provides the specialists, but clients maintain complete control over the project. Essentially, this model lets businesses swiftly plug any skill gaps or address resource shortages without a hitch. The external professionals blend seamlessly with the core team, effectively becoming part of it for the project's duration. When compared to the outstaffing model, which is more about "leasing" specialists, staff augmentation focuses on directly integrating these specialists into the in-house team for specific tasks. Key Benefits of the Staff Augmentation Model: Swift access to specialized expertise only for the time you need it. Financially smart, allowing cost savings without skimping on quality. Clients retain complete control over both the team and the project's progress. Things to Keep in Mind: Clients bear the entire responsibility for the project's success or setbacks. Resources might be needed to manage the augmented staff and ensure they mesh well with the in-house team. It is crucial to bridge any communication gaps and have a solid onboarding process in place. When is the Staff Augmentation Model the Right Choice? Consider this model when your project needs specific expertise that only a few professionals can offer rather than a whole team. It is especially apt when close, personal collaboration is pivotal. 4. Offshore Development Center (ODC) Model The Offshore Development Center model is essentially about partnering with an external IT unit located in a different region. The beauty of an ODC lies in its location; often in countries with favorable economic conditions, a robust pool of talented professionals, and a culture that fosters innovation. ODCs often handle infrastructure, administration, and HR processes independently, relieving clients from these operational responsibilities. Essentially, the ODC Model establishes an entire unit or department offshore, offering a more holistic and self-contained solution. The offshore team typically immerses itself in the client's corporate culture and dedicates its time to a single project. This arrangement offers the perk of tapping into various time zones, extending the business's working hours. Key Benefits of the Offshore Development Center Model: Savings on expenses related to equipment purchases, workspace setups, and the onboarding process. Offloads the responsibility of recruitment and the accompanying administrative costs to the service provider. Flexibility to adjust the team's size at any project phase. Grants a degree of oversight on the project's progression. Things to Keep in Mind: It might not be the best fit for short-term projects with set-in-stone requirements When is the Offshore Development Center Model the Right Choice? Lean toward this engagement model if you aim to establish a full-fledged IT department with a development team but prefer to sidestep the intricacies of recruitment, infrastructure setup, and the administrative overheads that come with it. 5. Hybrid Onshore-Offshore Model The Hybrid Onshore-Offshore engagement model marries the strengths of both onshore and offshore development strategies. Essentially, it integrates on-site management with offshore development teams. This model facilitates a distribution of tasks between local and distant teams. So, while on-the-ground management interacts closely with the client, the bulk of the developmental tasks are undertaken by offshore experts. Typically, on-site employees handle about 25% of the tasks, leaving the remaining 75% to offshore teams. Key Benefits of the Hybrid Onshore-Offshore Model: Superior quality derived from a blend of top global talents and local expertise for oversight. Cost advantages, courtesy of tapping into offshore resources and minimizing infrastructure expenses. Fewer bureaucratic hurdles since only a part of the tasks are outsourced internationally. Things to Keep in Mind: The logistical and administrative overhead of managing both on-site and offshore teams. Potential cultural and communication nuances between the local and overseas teams. When is the Hybrid Onshore-Offshore Model the Right Choice? Opt for this engagement model if you are tackling a complex, long-term IT project that could benefit from immediate local guidance and the specialized input of international experts. 6. Fixed-Price Model The fixed-price engagement model revolves around a pre-established project budget with a well-defined scope, objectives, deliverables, and deadlines. Success in this model hinges on meticulous planning, thorough assessment, and detailed analysis from the get-go, given that any mid-course alterations can be quite challenging. Using the fixed-price approach, clients are fully aware of their expenditure for the project upfront. The entire endeavor adheres strictly to the contractual agreement in terms of scope and cost. Key Benefits of the Fixed-Price Model: A locked-in cost set before the project's start, unaffected by the number of workers or other variables. Consistent progress monitoring and diminished risks thanks to a thoroughly defined project outline. Precise timelines and set deadlines. Things to Keep in Mind: Even minor changes to the project's scope can result in notable cost escalations. A detailed and clear technical specification is vital. Excellent planning from the client's end and robust management by the service provider are essential. When is the Fixed-Price Model the Right Choice? Consider this engagement model for short-term, small to medium-sized projects where you have a crystallized idea with specific tasks and expected outcomes, and there is little to no chance of alterations. Choosing the Right Engagement Model for You Finding the perfect engagement model is not a one-size-fits-all endeavor. Yet, with a strategic mindset, you can identify the ideal fit tailored to your objectives and requirements. Here is a step-by-step guide: Pinpoint your project's goals, scope, desired outcomes, timelines, and tasks. Gauge the possibility of scaling your project or making adjustments as it progresses. Think about your staffing requirements. Do you need a few specialists or an entire team? If you have outsourced before, let your past experiences guide your decision. If not, consult with those who have such experience. Even if a project seems short-term now, always keep an open mind. Circumstances and goals can evolve; always think ahead. Conclusion Go back to my breakdown of the different engagement models, then select the one that resonates most with your situation. As you embark on this journey, ensure you collaborate with a trusted software development provider. Always remember: maintaining open communication lines and striving for mutual goals are the hallmarks of a fruitful partnership.

Relational DataBase Management Systems (RDBMS) represent the state-of-the-art, thanks in part to their well-established ecosystem of surrounding technologies, tools, and widespread professional skills. During this era of technological revolution encompassing both Information Technology (IT) and Operational Technology (OT), it is widely recognized that significant challenges arise concerning performance, particularly in specific use cases where NoSQL solutions outperform traditional approaches. Indeed, the market offers many NoSQL DBMS solutions interpreting and exploiting a variety of different data models: Key-value store (e.g., the simplest storage where the access to persisted data must be instantaneous and the retrieve is made by keys like a hash-map or a dictionary); Documented-oriented (e.g., widely adopted in server-less solutions and lambda functions architectures where clients need a well-structured DTO directly from the database); Graph-oriented (e.g., useful for knowledge management, semantic web, or social networks); Column-oriented (e.g., providing highly optimized “ready-to-use” data projections in query-driven modeling approaches); Time series (e.g., for handling sensors and sample data in the Internet of Things scenarios); Multi-model store (e.g., combining different types of data models for mixed functional purposes). "Errors using inadequate data are much less than those using no data at all."CHARLES BABBAGE A less-explored concern is the ability of software architectures relying on relational solutions to flexibly adapt to rapid and frequent changes in the software domain and functional requirements. This challenge is exacerbated by Agile-like software development methodologies that aim at satisfying the customer in dealing with continuous emerging demands led by its business market. In particular, RDBMS, by their very nature, may suffer when software requirements change over time, inducing rapid effects over database tabular schemas by introducing new association tables -also replacing pre-existent foreign keys- and producing new JOIN clauses in SQL queries, thus resulting in more complex and less maintainable solutions. In our enterprise experience, we have successfully implemented and experimented with a graph-oriented DBMS solution based on the Neo4j Graph Database so as to attenuate architectural consequences of requirements changes within an operational context typical of a digital social community with different users and roles. In this article, we: Exemplify how graph-oriented DBMS is more resilient to functional requirements; Discuss the feasibility of adopting graph-oriented DBMSs in a classic N-tier (layered) architecture, proposing some approach for overcoming main difficulties; Highlight advantages and disadvantages and threats to their adoption in various contexts and use cases. The Neo4j Graph Database The idea behind graph-oriented data models is to adopt a native approach for handling entities (i.e., nodes) and relationships behind them (i.e., edges) so as to query the knowledge base (namely, knowledge graph) by navigating relationships between entities. The Neo4j Graph Database works on oriented property graphs where both nodes and edges own different kinds of property attributes. We choose it as DBMS, primarily for: Its “native” implementation is concretely modeled through a digital graph meta-model, whose runtime instance is composed of nodes (containing the entities with their attributes of the domain) and edges (representing navigable relationships among the interconnected concepts).In this way, relationships are traversed in O(1); The Cypher query language, adopted as a very powerful and intuitive query system of the persisted knowledge within the graph. Furthermore, the Neo4j Graph Database also offers Java libraries for Object Graph Mapping (OGM), which help developers in the automated process of mapping, persisting, and managing model entities, nodes, and relationships. Practically, OGM interprets, for graph-oriented DBMS, the same role that the pattern Object Relational Mapping (ORM) has for relational persistence layers. Comparable to the ORM pattern designed for RDBMS, the OGM pattern serves to streamline the implementation of Data Access Objects (DAOs).Its primary function is to enable semi-automated elaboration in persisting domain model entities that are properly configured and annotated within the source code. With respect to Java Persistence API (JPA)/Hibernate, widely recognized as a leading ORM technology, Neo4j's OGM library operates in a distinctive manner: Write Operations OGM propagates persistence changes across all relationships of a managed entity (analyzing the whole tree of objects relationships starting from the managed object); JPA performs updates table by table, starting from the managed entity and handling relationships based on cascade configurations. Read Operations OGM retrieves an entire "tree of relationships" with a fixed depth by the query, starting from the specified node, acting as the "root of the tree"; JPA allows the configuration of relationships between an EAGER and a LAZY loading approach. Solution Benefits of an Exemplary Case Study To exemplify the meaning of our analysis, we introduce a simple operative scenario: the UML Class Diagram of Fig. 1.1 depicts an entity User which has a 1-to-N relationship with the entity Auth (abbr. of Authorization), which defines permissions and grants inside the application.This Domain Model may be supported in RDBMS by a schema like that of Tab. 1.1 and Tab. 1.2 or, in graph-oriented DBMS, as in the knowledge graph of Fig. 1.2. Fig. 1.1: UML Class Diagram of the Domain Model. users table id firstName lastName ... ... ... Tab. 1.1: Table mapped within RDBMS schema for User entity. AUTHS table id name level user_fk ... ... ... ... Tab. 1.2: Table mapped within RDBMS schema for Auth entity. Fig. 1.2: Knowledge graph related to the Domain Model of Fig. 1.1. Now, imagine that a new requirement emerges during the production lifecycle of the application: the customer, for administrative reasons, needs to bound authorizations in specific time periods (i.e., from and until the date of validity) as in Fig. 2.1, transforming the relationship between User and Auth in a N-to-N. This Domain Model may be supported in RDBMS by a schema like that of Tab. 2.1 or, in graph-oriented DBMS, as in the knowledge graph of Fig. 2.2. Fig. 2.1: UML Class Diagram of the Domain Model after the definition of new requirements. users table id firstName lastName ... ... ... Tab. 2.1: Table mapped within RDBMS schema for User entity. users_AUTHS table user_fk auth_fk from until ... ... ... ... Tab. 2.2: Table mapped within RDBMS schema for storing associations between User and Auth. entities. AUTHS table id name level ... ... ... Tab. 2.3: Table mapped within RDBMS schema for Auth entity. Fig. 2.2: Knowledge graph related to the Domain Model of Fig. 2.1. The advantage is already clear at a schema level: indeed, the graph-oriented approach did not change the schema but only prescribes the definition of two new properties on the edge (modeling the relationship), while the RDBMS approach has created the new association table users_auths substituting the external foreign key in auths table referencing the user's table. Proceeding further with a deeper analysis, we can try to analyze a SQL query wrt a query written in the Cypher query language syntax under the two approaches: we’d like to identify users with the first name “Paul” having an Auth named “admin” with the level greater than or equal to 3. On the one hand, in SQL, the required queries (respectively the first one for retrieving data from Tab. 1.1 and Tab. 1.2, while the second one for Tab. 2.1, Tab. 2.2, and Tab. 2.3) are: SQL SELECT users.* FROM users INNER JOIN auths ON users.id = auths.user_fk WHERE users.firstName = 'Paul' AND auths.name = 'admin' AND auths.level >= 3 SQL SELECT users.* FROM users INNER JOIN users_auths ON users.id = users_auths.user_fk INNER JOIN auths ON auths.id = users_auths.auth_fk WHERE users.firstName = 'Paul' AND auths.name = 'admin' AND auths.level >= 3 On the other hand, in Cypher query language, the required query (for both cases) is: Cypher MATCH (u:User)-[:HAS_AUTH]->(auth:Auth) WHERE u.firstName = 'Paul' AND auth.name = 'admin' AND auth.level >= 3 RETURN u While the SQL query needs one more JOIN clause, it can be noted that, in this specific case, not only the query written in Cypher query language does not present an additional clause or a variation on the MATCH path, but it also remains identical. No changes were necessary on the "query system" of the backend! Conclusions Wedge Engineering contributed as the technological partner within an international Project where a collaborative social platform has been designed as a decoupled Web Application in a 3-tier architecture composed of: A backend module, a layered RESTful architecture, leveraging on the JakartaEE framework; A knowledge graph, the NoSQL provided by the Neo4j Graph Database; A frontend module, a single-page app based on HTML, CSS, and JavaScript, exploiting the Angular framework. The most challenging design choice we had to face was about using a driver that exploits natively the Cypher query language or leveraging on the OGM library to simplify DAO implementations: we discovered that building an entire application with custom queries written in Cypher query language is neither feasible nor scalable at all, while OGM may be not efficient enough when dealing with large data hierarchies that involve a significant number of relationships involving referenced external entities. We finally opted for a custom approach exploiting OGM as the reference solutions for mapping nodes and edges in an ORM-like perspective and supporting the implementation of ad hoc DAOs, therefore optimizing punctually with custom query methods that were incapable of performing well. In conclusion, we can claim that the adopted software architecture well responded to changes in the knowledge graph schema and completely fulfilled customer needs while easing efforts made by the Wedge Engineering developers team. Nevertheless, some threats have to be considered before adopting this architecture: SQL is far more common expertise than Cypher query language → so it’s much easier to find -and thus to include within a development team- experts able to maintain code for RDBMS rather than for theNeo4j Graph Database; Neo4j system requirements for on-premise production are relevant (i.e., for server-based environments, at least 8 GB are recommended) → this solution may not be the best fit for limited resources scenarios and for low-cost implementations; At the best of our efforts, we didn’t find any open source editor “ready and easy to use” for navigating through the Neo4j Graph Database data structure (the official data browser of Neo4j does not allow data modifications through the GUI without custom MERGE/CREATE query) as there are many for RDBMS → this may be intrinsically caused by the characteristic data model which hardens the realization of tabular views of data.

TL; DR: Can We Or Should We Change Scrum? Can we or should we change Scrum, or is it blasphemy to tweak the "immutable" framework to accommodate our teams’ and organizations’ needs? Not so fast; don’t just dismiss augmenting Scrum as leaving the path, contributing to the numerous Scrumbut mutations, giving Scrum a bad name. However, in our rapidly evolving business landscape, sticking rigidly to traditional Scrum by the book could be a straightjacket stifling innovation, user focus, and adaptability. From ensuring cultural compatibility to facing technical debt challenges and emerging technologies, discover ten compelling reasons why augmenting Scrum isn’t just okay — it’s necessary for modern teams. Read on to discover when and how to adapt Scrum responsibly without diluting its essence. Reasons for Changing Scrum There are multiple legitimate reasons why you may consider to change Scrum: Business Complexity: Modern business complexity often exceeds standard Scrum’s scope. Organizations often face interdependencies among departments and other entities, third-party vendors, or regulatory bodies. Enhancing Scrum to consider these elements allows for a more holistic approach to solving customer problems sustainably. Compliance and Regulation: In highly regulated industries, additional checks and balances are needed. Scrum can be augmented to meet compliance needs, for example, by specialized Developer roles responsible for ensuring that regulatory requirements are met. Integrating With Other Methodologies: Many organizations employ multiple agile frameworks or methodologies. Modifying Scrum to better integrate with, for example, Kanban in maintenance projects or Design Thinking in early-stage product development can create a more cohesive, effective process flow. Innovation: Scrum is designed for incremental improvement but isn’t necessarily geared for groundbreaking innovation. Incorporating elements that promote innovation, like “innovation Sprints” or hackathons, can add a new dimension to what Scrum teams can achieve. Resource Constraints: Smaller organizations or teams with limited resources might find it challenging to follow Scrum by the book. Simplifying or tweaking Scrum elements can help organizations adopt agile practices without being overwhelmed. Product Discovery: Scrum is often criticized for lacking explicit guidance on product discovery. Adding a discovery phase or supporting the Product Owner to focus on this aspect can ensure that the Scrum team is building the right product, not just building the product right. User Experience Focus: Traditional Scrum doesn’t explicitly emphasize user experience (UX). But as UX gains importance in software development, there is a growing need to incorporate it within the Scrum framework, which means integrating user testing and design into the Sprint flow. Data-Informed Decisions: Scrum emphasizes stakeholder feedback but doesn’t necessarily prescribe data-informed decision-making. Integrating data analytics into Scrum can help teams be more objective and precise in planning and execution. (Scrum.org points the way with its Evidence-Based Management approach.) Remote Work Challenges: The recent surge in remote work brings its own set of challenges. Changing Scrum to adapt to remote team dynamics, such as asynchronous communication or tools for remote collaboration, is almost necessary. When examined critically, each of these reasons to change Scrum shows that the framework, while robust, might only partially cover the array of challenges and opportunities product teams encounter. Consequently, as long as changes are made thoughtfully and respectfully to the framework’s first principles, a solid argument must be made for its augmentation. Conditions for Changing Scrum Let’s delve deeper into the conditions under which changing Scrum could be deemed acceptable: Holistic View: No changes should not be made in isolation. We must consider how a change in one area might impact others. A holistic view ensures that modifications to Scrum are coherent and synergistic rather than disruptive or conflicting. Complete Understanding of Scrum Principles: Before making any changes to Scrum, it’s crucial to thoroughly understand its core principles and practices. A solid grasp ensures that any adjustments made will not undermine the fundamental tenets of Scrum. Organizational Alignment: The change should not only benefit the Scrum team but should also be in line with the larger organizational strategy. Disconnection between the Scrum team’s practices and organizational objectives can result in friction and hamper customer value creation. Customer-Centric: The change should be aligned with the ultimate goal of delivering more value to the customer. If a proposed alteration does not improve the product or make the process more customer-centric, the change needs reversal. Ethical and Legal Compliance: Changes should not introduce practices that violate legal or ethical guidelines, including labor laws, industry regulations, and corporate governance guidelines. Clear Objectives: Any change should have a clear, well-defined objective to solve a specific problem or improve an aspect of the workflow. This objective should be measurable and aligned with the overall goals of the project or the organization. Team Consensus: Scrum emphasizes collective decision-making. The team should discuss and agree upon any changes to the framework. Iterative Experimentation: The Scrum team or teams should test any significant changes in a smaller scope before implementing them to gauge their effectiveness. An experimental approach allows for modifications and quick reversals if the change proves to be ineffective or detrimental. Data-Backed Rationale: The Scrum teams should use empirical data to justify the change. For example, employ stakeholder satisfaction surveys post-release to justify modifications in stakeholder engagement practices, ensuring that the developed product aligns well with stakeholder expectations and organizational objectives. Review and Feedback: A team should review the situation regularly to assess its impact after implementing a change. The review would include feedback from all team members and stakeholders to evaluate the effectiveness of the previous change. Consequently, the team needs to reverse any ineffective changes. So, there are legitimate reasons to change Scrum, and we can define conditions that will support a change while respecting Scrum’s first principles. Let’s Change Scrum Now, let us navigate nuanced Scrum augmentations to suit organizational contexts better, addressing ten issues to uphold agile principles amidst varied operational scenarios: Leadership Buy-in: Leadership’s endorsement is crucial for any change in practice to be accepted and implemented effectively. You might need to adapt Scrum practices to meet certain management expectations or to secure resources, but this should never compromise agile principles. Demonstrating the ROI of proposed changes can be instrumental in gaining leadership support. (Example: Including a governance entity in the release process.) Cultural Compatibility: Organizations with unique cultures might not naturally align with Scrum’s principles. Tweaking the framework to fit an organization’s cultural norms isn’t about undermining Scrum’s integrity but ensuring its applicability and acceptability across diverse work environments. (Example: Creating a Sprint report next to having a Sprint Review.) Psychological Safety: A psychologically safe environment is crucial for team members to take risks, make mistakes, and learn from them. Though Scrum implies this through its emphasis on collaboration and respect, making it explicit through regular check-ins or specific team agreements can cement this critical aspect of agile work environments. Stakeholder Engagement: Scrum mentions the roles of the Product Owner and the Developers but leaves stakeholder engagement rather vague. Expanding Scrum to include customer feedback loops or internal stakeholder check-ins can add another layer of validation and alignment, ensuring that the product development is more in tune with end-user needs and organizational goals. (Example: Including stakeholders in Product Backlog management issues; for example, with User Story Mapping exercises.) Scalability Concerns: Scrum alone doesn’t offer guidance on operating at scale. Frameworks like Nexus and LeSS have provided their interpretations of scaled agility, which involve significant modifications to vanilla Scrum. Understanding when and how to use such frameworks requires an analysis of the specific organizational context. (Note: I do not consider SAFe® a suitable way of scaling Scrum.) Global Teams: In a worldwide setup, time zones, language, and cultural nuances can throw several challenges. Modifying Scrum to include asynchronous Daily Scrums or creating overlapping “core hours” where the whole team is available can be beneficial. These adjustments allow for smoother collaboration and effective communication among distributed team members. (Note: Scrum addresses primarily a single, co-located team.) Technical Debt: The accumulation of technical debt can stall progress and compromise quality. While Scrum doesn’t explicitly deal with this, modifying the framework to include dedicated time for resolving technical debt during each Sprint can create a healthier, more sustainable codebase. This allows teams to maintain a balance between feature delivery and code quality, thereby mitigating future risks. Emerging Technologies: As technology evolves rapidly, Scrum teams must adapt to incorporate new tools and techniques. Whether integrating data analytics into the Product Backlog prioritization process or incorporating AI-based testing tools, the framework should be flexible enough to accommodate technological advancements without losing its essence. (Note: Technical R&D should regularly be part of every Sprint.) Feedback Loops Beyond Retrospectives: Scrum relies heavily on Retrospectives for feedback. However, continuous feedback mechanisms focused on improvement, peer reviews, or customer validation loops could supplement the Retrospectives. Adding different feedback opportunities ensures that insights and improvements are ongoing rather than confined to Sprint boundaries, encouraging real-time growth and adaptation. (Example: Entertain occasional yet regular stakeholder and team Retrospectives.) Skill Set Diversification: For teams that still need to become cross-functional, consider adaptations to the Scrum framework to account for learning curves, upskilling, pairing with experts, and overcoming or compensating for organizational design debt. This proactive approach ensures that the team becomes truly self-sufficient over time. (Example: Overcoming the separation of user research or quality assurance from Scrum teams.) Each of these aspects offers a rich area for exploration and adaptation, and aligning them carefully with the core tenets of Scrum can ensure a more holistic, hands-on application of the framework. When Not to Change Scrum Finally, let’s have a look at change anti-patterns — the four main reasons not to tinker with Scrum’s process: Impatience for Quick Wins: Scrum is not a silver bullet but a framework that facilitates a particular process and culture. Adopting Scrum requires a period of adjustment and learning. Organizations or teams impatient for immediate results might tinker with the framework to expedite outcomes. This kind of impatience can lead to cutting corners, which often undermines the principles of Scrum and can result in long-term failures or sub-standard performance. Fundamental Misunderstanding: This comes from either not fully grasping the principles of Scrum or misconceiving it as a project management tool rather than a product development framework. When changes are made from the point of misunderstanding, they can dilute the essence of Scrum, leading to a mishmash of practices that defy the core tenets of agility, transparency, and collaboration. Personality-Driven Changes: In some teams, influential individuals or subgroups might push for changes that cater to their preferences or working styles rather than considering the team’s or project’s best interests. These personality-driven changes can lead to an uneven distribution of power or responsibility, eroding the collaborative fabric essential for Scrum. Trend Following or Buzzword Compliance: The agile world is not immune to trends and buzzwords. Whether it’s new roles, tools, or practices, there’s always something that’s capturing the industry’s imagination. Integrating these trends without critical evaluation can result in a confusing hybrid of methods that lack coherence. It can turn Scrum into an unrecognizable patchwork that fails to improve value creation and will likely introduce new problems. Understanding what not to do is equally as important as knowing what to do. Before you change Scrum, it’s crucial to evaluate whether the motivation behind the change is aligned with solving real problems and improving the process rather than stemming from one of these change anti-patterns. Conclusion Let’s not romanticize Scrum as some untouchable monolith; it’s a tool, not a religion. We’re not paid to do Scrum; we’re paid to deliver value and solve complex adaptive problems. In a constantly evolving world, our approaches to problem-solving and value delivery need to change, too. While the Scrum framework has proven remarkably resilient and effective, it is not a one-size-fits-all solution. It’s important to remember that Scrum serves the team, the product, and the organization in this sequence — not the other way around. The ultimate goal isn’t to implement Scrum perfectly but to solve real problems for real people in the most effective way possible. This sometimes requires adapting Scrum to fit our unique contexts better. What’s crucial is that any changes are made with a complete understanding of Scrum’s first principles so that we respect its essence while leveraging its structure for even greater success. So, don’t be afraid to innovate, adapt, and iterate — not just your product but also your process.

The abstract of this study has established trust is a crucial change for developers, and it develops trust among developers working at the different sites that facilitate team collaborations when discussing the development of distributed software. The existing research focused on how effectively to spread and build trust in not the presence of face-to-face and direct communications that overlooked the effects of trust propensity, which means traits of different personalities representing an individual disposition to perceive another as trustworthy. The preliminary quantitative analysis has been presented in this study to analyze how the trust propensity affects the collaboration success in the different distributed projects in software engineering projects. Here, the success is mainly represented through the request of pull that codes contribute, and changes are successfully merged to the repository projects. 1. Introduction In global software engineering, trust is considered the critical factor affecting software engineering success globally. However, decreased trust in software engineering has been reported to: Aggravate the separated team's feelings by developing conflicting goals Reduce the willingness to cooperate and share information to resolve issues Affect the nature of goodwill in perspective towards others in case of disagreements and objections [1] Face-to-face interactions (F2F) in current times help to grow trust among team members, and they successfully gain awareness related to both in terms of personal aspects and technical aspects [2]. On the contrary, the F2F interaction is more active now, which may be reduced in software-distributed projects [3]. Previous empirical research has shown that online or social media interactions among members over chat or email build trust among team members of OSS (open-source and software projects) that mainly have no chance related to meeting with team members [4]. Furthermore, a necessary aspect, especially for better understanding the trust development and cooperation in the working team, is the simple way of "propensity to trust" [5]. It is spreading the personal disposition of an individual or trust or taking the risk internally addicted to the trustee's beliefs and will behave as evaluated and expected [6]. However, the propensity of trust also refers to team members or individuals who generally tend to be perceived by others as trustworthy [7]. Moreover, we formulated the supporting research questions for this study that include as: Research Question (RQ): How does the propensity of trust of individuals facilitate successful collaboration even in software globally distributed projects? The most common limitation of this research is its relevance to empirical research findings based on supporting trust [8]. The trust represents no explicit extent measures to which individual developers' trust directly contributed to the performances of projects [9]. However, in this study, the team mainly intended to overcome the supporting limitation by approximating overall project performances, including duration, productivity, and completion of requirements. Through successful collaborations, the researcher's team indicated a situation where mainly two developers are working together and developed cooperation successfully due to yielding the supporting project advancement through adding new features or fixing bugs. By such nuanced and supporting gain unit analysis, the research aim is to measure more directly how trust facilitates cooperation in distributed software projects. Modern distributed and software projects support their coordination and workflow in remote work with the best version of control systems. The supporting pull request is considered the popular best way to successfully submit contributions to projects by using the distributed new version of the control system called Git. Furthermore, in reference to the development of pull-based development and model [10], the central repository of projects is to avoid the share among the different developers. On the contrary, developers mainly contributed through forking, that is, cloning repositories and successfully making the changes from each other. The primary condition and working of the pull repository is that when a set of supporting changes is successfully ready to finally submit the results into the central repository at that time, potential contributors mainly create the supporting pull request. After that, an integration manager, also known as a core developer, is effectively assigned the responsibility to individuals to inspect the integrated change in line with the project's central development. While working on software-distributed projects, an integration manager's main role is to ensure the best project quality. However, after the pull repository contribution's successful receipt, the pull request was closed, which is suitable for projects. That means the request of pull is either changed and accepted are integrated into the repository of the leading project or can be considered incorrect that considered as the pull request is changed or declined are rejected. Whether declined or accepted, the request for closed pull requires that the consensus is successfully reached by discussion. Furthermore, project managers also use the development collaborative platforms, including Bitbucket and GitHub, that make projects easier for developers or team members to collaborate by pull request [11]. It helps to provide a supporting and user-friendly interface web environment for discussing the supporting or proposed changes before the supporting integrating them into the successful source code of the project. Accordingly, researchers represent the successful collaborations between the individual developers concerning accepting the request of pull and refine the best research question as supporting or follows: Research Question (RQ):How does the propensity of trust of individuals facilitate successful collaboration even in software globally distributed projects? The researchers successfully investigated the refined questions of this research by analyzing the contributions history of pull requests from the famous developers of project Apache Groovy. Apache Groovy mainly provides archived supporting history through email-dependent communications. Researchers analyze the trace interactions over the supporting channels that assess the developers in an effective way known as “Propensity to trust." The remainder of this research paper is well organized: the next section mainly discusses the challenge and supporting solutions to propensity quantifying trust. In sections three and four, researchers described the supporting empirical study and related its results. In the fifth section, researchers discuss the limitations and findings. At last, the research finally drew a conclusion and better described the future in the sections of this research. 2. Background Measuring the Term “Propensity To Trust” The five-factor or big five personality effective model is used as a general taxonomy to evaluate personality traits [12] mentioned in Figure 1. It includes supporting higher levels such as openness, extraversion, conscientiousness, neuroticism, and agreeableness. However, each higher-level dimension has six supporting subdimensions that are further evaluated according to the supporting dimensions [13]. Previous research has confirmed personality traits that can be successfully derived from practical analysis of emails or written text [14]. According to Tausczik & Pennebaker [15], in the big five models, each trait is significantly and strongly associated with theoretically appropriated word usage patterns that indicate the most substantial connection between personality and language use [16]. Figure 1: Big-Five traits of personality model (Source: Developed by learner) Furthermore, the existing research based on trust has directly relied on data self-reported through survey questionnaires that help to measure the trust of individuals on the supporting given scale [17], [18], and [19]. In addition, one of the reliable and notable exceptions is mainly represented through the supporting work of Wang and Redmiles [7], who successfully studied how trusts mainly spread in the supporting OSS projects. The researchers use the word count and linguistic inquiry psycholinguistics dictionary to help analyze and better use in supporting writing [15] and [20]. The trust quantitative measure is obtained based on the critical term “Tone Analyser”, an LIWC IBM Watson service leveraging. However, which uses the supporting linguistic analysis that helps to detect the three significant types of supporting tones from supporting written text, including writing, emotional, and social style, it is significantly driven by social tone measures to connect with the social tendencies in supporting the people writing (that is a trait of a big personality). In supporting ways, researchers focused on agreeableness recognition, one of the personality traits that indicated the tendency of people to be cooperative and compassionate towards others. However, one supporting trust related to agreeableness is trusting others to avoid being suspicious [21] efficiently. About the following, the researcher uses agreeable personality traits similar to proxy supporting measures of an individual's propensity to trust. Factors Influenced the Acceptance of Pull-Requests According to [22] and [23], the factors influencing the contribution acceptances related to requests are considered both technical and social. Both factors include technical and social factors explained in context with the pull request. Technical aspects, the existing research based on patch acceptance as mentioned in [24], code reviewing, and bug training as mentioned in [25] and [26], has analyzed that the contribution of merge decision is directly affected through both project size such as team and KLOC size including the patch itself. Similarly, according to [10], it was analyzed that approximately 13% of pull requests reviewed were successfully closed to avoid merging, especially for supporting or purely technical reasons. However, the researchers found the supporting decision related to merging was mainly affected by the changes involving supporting code areas actively under coverages and developing test attached cases. With the increasing demand for social and transparent coding platforms, including GitHub and Bitbucket, integrators refer to contribution quality by looking at both technical and quality and developing track records through acceptance of previous contributions [27]. Numbers of followers and stars in GitHub reputations as auxiliary indicators [28]. However, the iterating findings related to pulling requests are explored as "treated equally" related to submitters defined as "social status." That is whether the external contributors are the core team members' core development. Furthermore, Ducheneaut [30] examined that the main contribution directly comes from the submitters, who are also recognized as the core development teams, driving the higher chances of acceptance and using the supporting records of signal interactions for judging and driving the proposed changes in quality. Finally, the supporting findings help to provide compelling further motivations to others for looking at the non-technical and other factors that can influence the decision to merge the supporting pull request effectively. 3. Empirical Study Researchers designed the study effectively to quantitatively access and analyze the impact of trust propensity to pull requests or (PRs) acceptance. However, the researchers used the simple and logistic regression that helped to build the model, especially for estimating the success or probability of the supporting merged or pull request that given the propensity integrators to trust. That means it is agreeableness as measured through the analysis of IBM Watson Tone. Moreover, in our supporting framework, researchers treat the pull request acceptances as the supporting dependent variables, including the independent variables as measures of agreeableness integrator that is a predictor. For supporting the study, the two primary resources are successfully used to collect the data information that is pulled, especially in GitHub, and the second emails retrieved, especially from the project of Apache Groovy. The project is considered object-oriented programming used for the Java platform, and scripting languages are also used for the Java platform. However, among several projects, this project is supported through Apache Software Foundations. Researchers opportunistically chose Groovy due to: It can make faster mailing and archives freely accessible. It can follow a supporting pull request and rely on the development model. Dataset The researchers used the supporting database of GHTorrent to collect the information in chronological order to support lists of pull requests [31]. It opened based on GitHub, and for each request of pull, researchers stored the supporting information, including: The main contributor The supporting data when it was accessible or opened The merged status The integrator The supporting data when it merged or closed Not all the pull requests are supported by GitHub. The researchers looked at the comments of pull requests, as mentioned, to identify the closed and merged that are outside of the supporting GitHub. In support, the researchers searched especially for the presences related to: Main branch commits that closed related to pulling requests Comments, especially from the manager's integration who supported the acknowledgment of a successful merge Researchers reviewed all the pull request projects one by one and evaluated their status annotated and manually. Automated Albeit reviews similar procedures that help describe in [10]. Furthermore, the description related to the Apache Groovy project is shown in Table 1. DESCRIPTION FOR JAVA-SUPPORTING PLATFORMS, OBJECT-ORIENTED PROGRAMMING IS THE PREFERRED LANGUAGE Languages Java Number of project committers 12 PRs based on GitHub 476 Emails successfully achieved, especially in the mailing list 4,948 Unique email senders 367 Table 1: Apache Groovy Project description (Source: Developed by learner) From Table 1, researchers almost drive the 5,000 messages from project emails and use my stats' supporting tools to mine the supporting users and drive the mailing list. It is available on the websites of Groovy projects. The researchers first retrieve the committer's identities, which are core team members, including the writer who accesses the supporting repository. It was retrieved from the Groovy projects web page hosted at Apache and GitHub. Furthermore, researchers compared the names and IDs of users to integrate the pull request and shared the mailing list names. After that, researchers are able to identify the supporting messages from the ten integrators. Lastly, the team is filtered out by developers who mainly exchange up to 20 supporting mains in a specific time period. Integrators of Propensity To Trust Once the researchers successfully obtained the supporting mapping related to the communications records of team developers and core team members, they successfully computed the healthy scores of propensity to trust from the supporting content related to the entire corpus of emails. In support, researchers or developers process email content through the tone of Analyzer and obtain the agreeableness score. That is defined as the interval of 0 and 1. The supporting value is obviously smaller than one that is 0.5, associated with lower agreeableness. Therefore, it tends to be less cooperative and compassionate towards others. The values equal and more than 0.5 are directly connected with higher agreeableness. However, in the end, the high and low agreeableness scopes are considered by researchers or developers to drive the level of propensity trust integrators analyzed in Table 2. 4. Results In this section, researchers present the supporting results through a regression model to build a practical understanding of the propensity to trust. It is a predictor and pulls request acceptances. The researchers performed the regression simple logistics through R statistical and supporting packages. In Table 3, the analysis of results is reported, and researchers omit to evaluate the significant and positive efforts related to control variables, including #emails and #PRs reviewed to send because of space constraints. The results include the coefficient estimate related to + 1.49 and add odds ratio of 4.46 with drive statistical significance, including a p-value of 0.0009. Furthermore, the coefficient sign estimates and indicates the negative or positive associations related to the predictor, including the success of the pull request. In addition, the OR (odds ratio) weighs and drives the effect size that impacts closer to value one. It is a more negligible impact based on parameters based on the success of chance [32]. The results indicate trust propensity, which is significantly and positively associated with the probability of supporting pull requests that successfully merged. Furthermore, researchers can use the estimated coefficients from the supporting model explored in Table 2 and the equations below. It can directly affect the merging probability and pull requests from the project of Groovy. However, the estimated probability of acceptance PR includes the following: PR estimated probability: 1/ (1 + exponential (-(+1.49+4.46 * trust propensity))).............(i) From the above equation and example, the k pull request and probability acceptances through an integrator i with the supporting low propensity is 0.68. The overall PR correspondence increases as the results and supporting questions are analyzed. 5. Discussion The discussion section is the best analysis of knowledge evaluated by researchers in their first attempts to drive the quantifying effects on the developer's trust and personal traits. It is based on distributed software projects by following a pull request supporting a based development model. Similarly, the practical result of this study is to drive the initial evidence to evaluate the chances related to merging the contributions code that is correlated with the traits of personality of integrators who performed well in reviewing the code. Furthermore, the novel finding underlines the supporting role played by the researchers or developers related to the trust of propensity through personality related to the execution of review code and tasks. The discussion results are linked with primary sources such as [22] and [30], mainly observed through the social distance between the integrator and social contributors to influence the acceptance changes through pull requests. Integrators Reviewed PR:Merged Reviewed PR:Closed A score of propensity trust Developer 1 14 0 High Developer 2 57 6 Developer 3 99 7 High Developer 4 12 4 Low Developer 5 10 1 Low Developer 6 8 0 Low Total 200 18 - Table 2: Score of Propensity to trust include pull request (Source: Developed by learner) Predictors Estimate of Coefficient Odds Ratio Supporting P-Value Intercept Plus (+) 0.77 0.117 Trust Propensity Plus (+) 1.49 4.46 0.009 Table 3: Simple Logistic regression model results (Source: Developed by learner) From the supporting discussion and analysis of Tables 2 and 3, the developers mainly recommend making sure about the community before any contribution. The users mentioned in the supporting comments of the pull requests followed the recommendation explicitly to request the supporting review from the integrators [33]. It shows the willingness to help and cooperate with others to drive a higher trust propensity. The results and findings that PR accepted as p-value as 0.68. Moreover, the broader socio-technical framework congruences the research that finds the critical points for further studies and investigates far more as evaluated in research. The personality traits also match the needs of coordinates established with the help of technical domains supporting source code areas interested and evaluated with the proposed changes. Finally, in the supporting discussion, because of its preliminary nature, some limitations are also suffered in this study regarding the result's generalizability. The researchers mainly acknowledge the preliminary analysis based on supporting tools and numbers to involve developers from the side of single projects. However, only through the supporting replications, including the different values of datasets and settings, will researchers be able to evaluate and develop evidence on solid empirical changes. In addition, the other supporting limitation of this study is around the propensity validity and trust construct. Due to less practicality, researchers mainly decided to not only depend on the self-reported and traditional psychometric approaches that are used for measuring trust, including surveys [12]. In a replication of future perspective, researchers will support and investigate the reliability of analyzer tone services that connect with the technical domain that includes the software engineering from longer time linguistic resources, which are usually evaluated and trained based on the content of non-technical. 6. Conclusion and Recommendation Conclusion Therefore, the research has included six major chapters (sections) to evaluate the research topic based on a preliminary analysis of the propensity effect to trust in supporting distributed software development. The first chapter includes an overview of research supporting critical and real-time information. The second chapter includes personality traits and a significant five-factor analysis to build trust in teamwork. The third chapter has highlighted the empirical study related to research by connecting the real supporting projects such as Apache Groovy projects. The fourth chapter focuses on analysis and results to drive the research and projects. The five chapters have been discussed based on results and empirical study. The last chapter, or sixth chapter, concludes the research with supporting the recommendation for further study. This study represents the initial step in driving the broader research efforts that help collect the evidence of quantitative analysis and well-established trust among team members and developers and contribute to increased performances of projects related to distributed software engineering projects. In the analysis of personality models of the big five model, the trust propensity related to perceived others is driven by the trustworthy and stable personality traits that have changed from one person to another. Overall, the leveraging prior and supporting evidence that emerging personality traits unconsciously drive from the lexicon personally used in supporting written communications. The researchers and developers have used the tone of IBM Watson and analyzer services that help to measure the trust propensity by analysis of written emails archived through the projects of Apache Groovy. Furthermore, we found initial supporting evidence that researchers and developers who have posed with a higher propensity are likely to develop and trust more to accept the significant external contribution analyzed from the pull request. Recommendation For future work, it is recommended by researchers to replicate the supporting experiences to drive solid evidence. Researchers have followed and compared the analyzer tone, including the supporting tools, to better assess the reliability of research in extracting the personality, especially from the text that mainly contains the information of technical content. The researchers intended or used to enlarge the supporting database for both projects and supporting pull requests to understand the research better. It suggests that developers' personalities will change by relying on the project's participants and developing mutual trust through involving pairs of developers who mainly interact in supporting dyadic cooperation. For future study, it is suggested that developers of distributed software engineering projects use the network-centric approach, especially for estimating the supporting trust between software developers and open sources. Developers will follow the study's three stages of the network-centric approach to build trust while working from different physical locations globally on the distributed software engineering project. The first stage will be CDN (community-wide network developers) of this approach, which means community-wide network developers to construct better the information connecting to projects and developers. The second stage will compare the supporting trust between the supporting pairs directly linked to developers with the CDN. The last stage is computed with the trust between the developer pairs indirectly linked in the CDN. Figure 3: Future suggestion for a network-centric approach for estimating the trust (Source: Developed by learner) From Figure 3 above, CDN is the main stage connected with the other two stages to build trust and trustworthiness to drive potential contributions to an OSS project. The developers or researchers can construct the network-centric approach by focusing on CDN that provides supporting and valuable information about the effective collaboration between the OSS community and developers. The role of CDN in Figure 3 represents the community of developers driven by the OSS multiple projects that share some most common characteristics, including the same case and programming languages. Furthermore, developers or researchers can follow the main four features to label the supporting data to the regression train models for feature extractions. Word embedding can be used by developers such as Google Word2Vec to analyze and vectorize every comment to avoid the pre-trained model. The developers can train their own Word2Vec model related to data of software engineering to drive domain-specific and supporting models to better semantic representation analysis. It can be compared with the pre-trained and generic models. The developers can also use the 300 vector dimensional models to get comments and evaluate the vector representation. In addition, social can also be considered a strength for developers to build a connection between two or more developers to influence trust. Researchers and developers can assign the supporting integer values, especially for every role, to build the pull request and analyze the comment to build trust among individuals. References [1] B. Al-Ani, H. Wilensky, D. Redmiles, and E. Simmons, “An Understanding of the Role of Trust in Knowledge Seeking and Acceptance Practices in Distributed Development Teams,” in 2011 IEEE Sixth International Conference on Global Software Engineering, 2011. [2] F. Abbattista, F. Calefato, D. Gendarmi, and F. Lanubile, “Incorporating Social Software into Agile Distributed Development Environments.” Proc. 1st ASE Workshop on Social Sofware Engineering and Applications (SOSEA’08), 2008. [3] F. Calefato, F. Lanubile, N. Sanitate, and G. Santoro, “Augmenting social awareness in a collaborative development environment,” in Proceedings of the 4th international workshop on Social software engineering - SSE ’11, 2011. [4] F. Lanubile, F. Calefato, and C. Ebert, “Group Awareness in Global Software Engineering,” IEEE Softw., vol. 30, no. 2, pp. 18–23. [5] A. Guzzi, A. Bacchelli, M. Lanza, M. Pinzger, and A. van Deursen, “Communication in open source software development mailing lists,” in 2013 10th Working Conference on Mining Software Repositories (MSR), 2013. [6] Y. Wang and D. Redmiles, “Cheap talk, cooperation, and trust in global software engineering,” Empirical Software Engineering, 2015. [7] Y. Wang and D. Redmiles, “The Diffusion of Trust and Cooperation in Teams with Individuals’ Variations on Baseline Trust,” in Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 2016, pp. 303–318. [8] S. L. Jarvenpaa, K. Knoll, and D. E. Leidner, “Is Anybody out There? 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Welcome to our thorough Spring Boot interview questions guide! Spring Boot has grown in popularity in the Java ecosystem due to its ease of use and productivity boosts for developing Java applications. This post will present you with a curated set of often requested Spring Boot interview questions to help you ace your interviews, whether you are a newbie discovering Spring Boot or an experienced developer preparing for an interview. What Is Spring Boot? Spring Boot is an open-source Java framework built on top of the Spring framework. Spring Boot aims to make it easier to create stand-alone, production-ready applications with minimal setup and configuration. What Are the Major Advantages of Spring Boot? Spring Boot offers several significant advantages for Java application development. Some of the key advantages include: Simplified configuration: Spring Boot eliminates the need for manual configuration by providing sensible defaults and auto-configuration. It reduces the boilerplate code and enables developers to focus more on business logic. Rapid application development: Spring Boot provides a range of productivity-enhancing features, such as embedded servers, automatic dependency management, and hot reloading. These features accelerate development and reduce time-to-market. Opinionated approach: Spring Boot follows an opinionated approach, providing predefined conventions and best practices. It promotes consistency and reduces the cognitive load of making configuration decisions. Microservices-friendly: Spring Boot seamlessly integrates with Spring Cloud, facilitating the development of microservices architectures. It offers built-in support for service discovery, distributed configuration, load balancing, and more. Production-ready features: Spring Boot Actuator provides a set of production-ready features for monitoring, health checks, metrics, and security. It allows developers to gain insights into application performance and monitor system health easily. Embedded servers: Spring Boot comes with embedded servers like Tomcat, Jetty, and Undertow. This eliminates the need for manual server setup and configuration. Auto-configuration: Spring Boot’s auto-configuration feature automatically configures the application based on classpath dependencies. It simplifies the setup process and reduces the manual configuration effort. Community support: Spring Boot has a large and active community of developers. This means there is a wealth of resources, documentation, and community support available for troubleshooting and sharing best practices. Ecosystem integration: Spring Boot seamlessly integrates with other Spring projects and third-party libraries. It leverages the powerful Spring ecosystem, allowing developers to utilize a wide range of tools and libraries for various purposes. 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Starters simplify dependency management and help developers get started quickly with essential features such as web applications, data access, security, testing, and more. Embedded servers: Spring Boot includes embedded servlet containers like Tomcat, Jetty, and Undertow. These embedded servers allow developers to package the application as an executable JAR file, simplifying deployment and making the application self-contained. Spring Boot actuator: Spring Boot Actuator provides production-ready features for monitoring and managing the application. It offers endpoints for health checks, metrics, logging, tracing, and more. The actuator enables easy monitoring and management of the application in a production environment. Spring Boot CLI: The Spring Boot Command-Line Interface (CLI) allows developers to interact with Spring Boot applications using a command-line interface. It provides a convenient way to quickly prototype, develop, and test Spring Boot applications without the need for complex setup and configuration. Spring Boot DevTools: Spring Boot DevTools is a set of tools that enhance the development experience. It includes features like automatic application restart, live reload of static resources, and enhanced error reporting. DevTools significantly improves developer productivity and speeds up the development process. Spring Boot testing: Spring Boot provides excellent support for testing applications. It offers various testing utilities, including test slices, mock objects, and easy configuration for different testing frameworks. Spring Boot Testing makes it easier to write and execute tests for Spring Boot applications. What Are the Differences Between Spring and Spring Boot? Here’s a comparison between Spring and Spring Boot: Feature Spring Spring Boot Configuration Requires manual configuration Provides auto-configuration and sensible defaults Dependency Management Manual dependency management Simplified dependency management with starters XML Configuration Relies heavily on XML configuration Encourages the use of annotations and Java configuration Embedded Servers Requires manual setup and configuration Includes embedded servers for easy deployment Auto-configuration Limited auto-configuration capabilities Powerful auto-configuration for rapid development Development Time Longer development setup and configuration Faster development with out-of-the-box defaults Convention Over Configuration Emphasizes configuration Emphasizes convention over configuration Microservices Support Supports microservices architecture Provides seamless integration with Spring Cloud Testing Support Strong testing support with Spring Test Enhanced testing support with Spring Boot Test Actuator Actuator available as a separate module Actuator integrated into the core framework It’s important to note that Spring and Spring Boot are not mutually exclusive. 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For instance, the spring-boot-starter-web starter includes dependencies for web-related libraries and provides default configurations for handling web requests. Starters streamline dependency management and ensure that the required components work together seamlessly. By including a starter in your project, you save time and effort by avoiding manual configuration and gain the benefits of Spring Boot’s opinionated approach to application development. Examples of Commonly Used Spring Boot Starters Here are some examples of commonly used Spring Boot starters: spring-boot-starter-web: This starter is used for developing web applications with Spring MVC. It includes dependencies for handling HTTP requests, managing web sessions, and serving static resources. spring-boot-starter-data-jpa: This starter provides support for data access using Java Persistence API (JPA) with Hibernate as the default implementation. It includes dependencies for database connectivity, entity management, and transaction management. spring-boot-starter-security: This starter is used for adding security features to a Spring Boot application. It includes dependencies for authentication, authorization, and secure communication. spring-boot-starter-test: This starter is used for writing unit tests and integration tests in a Spring Boot application. It includes dependencies for testing frameworks like JUnit, Mockito, and Spring Test. spring-boot-starter-actuator: This starter adds production-ready features to monitor and manage the application. It includes endpoints for health checks, metrics, logging, and more. spring-boot-starter-data-redis: This starter is used for working with Redis, an in-memory data store. It includes dependencies for connecting to Redis server and performing data operations. spring-boot-starter-amqp: This starter provides support for messaging with Advanced Message Queuing Protocol (AMQP). It includes dependencies for messaging components like RabbitMQ. spring-boot-starter-mail: This starter is used for sending emails in a Spring Boot application. It includes dependencies for email-related functionalities. spring-boot-starter-cache: This starter provides support for caching data in a Spring Boot application. It includes dependencies for caching frameworks like Ehcache and Redis. spring-boot-starter-oauth2-client: This starter is used for implementing OAuth 2.0 client functionality in a Spring Boot application. It includes dependencies for interacting with OAuth 2.0 authorization servers. The entire list of Spring Boot starters can be found on the official Spring Boot website. Here’s the link to the official Spring Boot Starters documentation: Spring Boot Starters. What Is the Default Embedded Server Used by Spring Boot? The default embedded server used by Spring Boot is Apache Tomcat. Spring Boot includes Tomcat as a dependency and automatically configures it as the default embedded server when you use the spring-boot-starter-web starter or any other web-related starters. How Do You Configure Properties in a Spring Boot Application? In a Spring Boot application, properties can be configured using various methods. Here are the commonly used approaches: application.properties or application.yml file: Spring Boot allows you to define configuration properties in an application.properties file (for properties in key-value format) or an application.yml file (for properties in YAML format). Command-line arguments: Spring Boot supports configuring properties using command-line arguments. You can pass properties as command-line arguments in the format --property=valuewhen running the application. For example: java -jar myapp.jar --server.port=8080. Environment variables: Spring Boot can read properties from environment variables. You can define environment variables with property names and values, and Spring Boot will automatically map them to the corresponding properties. System properties: Spring Boot also supports configuring properties using system properties. You can pass system properties to the application using the -D flag when running the application. For example: java -jar myapp.jar -Dserver.port=8080. @ConfigurationProperties annotation: Spring Boot provides the @ConfigurationPropertiesannotation, which allows you to bind external properties directly to Java objects. You can define a configuration class annotated with @ConfigurationProperties and specify the prefix of the properties to be bound. Spring Boot will automatically map the properties to the corresponding fields of the configuration class. What Is Spring Boot Auto-Configuration, and How Does It Work? Spring Boot Auto-configuration automatically configures the application context based on the classpath dependencies, reducing the need for manual configuration. It scans the classpath for required libraries and sets up the necessary beans and components. Auto-configuration follows predefined rules and uses annotations like @ConditionalOnClass and @ConditionalOnMissingBean to enable configurations selectively. By adding the @EnableAutoConfiguration annotation to the main application class, Spring Boot triggers the auto-configuration process. Auto-configuration classes are typically packaged in starters, which contain the necessary configuration classes and dependencies. Including a starter in your project enables Spring Boot to automatically configure relevant components. The benefits of Spring Boot auto-configuration include: Reduced boilerplate code: Auto-configuration eliminates the need for manual configuration, reducing the amount of boilerplate code required to set up common functionalities. Opinionated defaults: Auto-configuration provides sensible defaults and conventions based on best practices. This allows developers to quickly get started with Spring Boot projects without spending time on manual configuration. Integration with third-party libraries: Auto-configuration seamlessly integrates with popular libraries and frameworks, automatically configuring the necessary beans and components required for their usage. Conditional configuration: Auto-configuration applies configurations conditionally based on the presence or absence of specific classes or beans. This ensures that conflicting configurations are avoided, and only relevant configurations are applied. How Can You Create a Spring Boot Application Using Spring Initializer? Creating a Spring Boot application is made easy by utilizing the Spring Initializr. The Spring Initializr is a web-based tool that generates a project template with all the necessary dependencies and configurations for a Spring Boot application. To create a Spring Boot application easily using the Spring Initializr, you can follow these steps: Visit the Spring Initializr Website: Go to the official Spring Initializr website at start.spring.io. Configure Project Settings: On the Spring Initializr website, you’ll find various options to configure your project. Provide the following details: Project: Select the project type (Maven or Gradle). Language: Choose the programming language (Java or Kotlin). Spring Boot Version: Select the desired version of Spring Boot. Project Metadata: Specify the group, artifact, and package name for your project. Add Dependencies: In the “Dependencies” section, you can search for and select the dependencies you need for your project. The Spring Initializr provides a wide range of options, such as Spring Web, Spring Data JPA, Spring Security, and more. You can also search for specific dependencies in the search bar. Generate the Project: Once you’ve configured the project settings and added the desired dependencies, click on the “Generate” button. The Spring Initializr will generate a downloadable project archive (a ZIP file) based on your selections. Extract the Project: Download the generated ZIP file and extract it to your desired location on your computer. Import the Project in your IDE: Open your preferred IDE (e.g., IntelliJ IDEA, Eclipse, or Visual Studio Code) and import the extracted project as a Maven or Gradle project. Start Developing: With the project imported, you can start developing your Spring Boot application. Add your application logic, create controllers, services, repositories, and other necessary components to implement your desired functionality. Run the Application: Use your IDE’s run configuration or command-line tools to run the Spring Boot application. The application will start, and you can access it using the provided URLs or endpoints. What Are Spring Boot Actuators? What Are Different Actuator Endpoints? Spring Boot Actuators are a set of production-ready management and monitoring tools provided by the Spring Boot framework. They enable you to monitor and interact with your Spring Boot application at runtime, providing valuable insights into its health, metrics, and various other aspects. Actuators expose a set of RESTful endpoints that allow you to access useful information and perform certain operations on your Spring Boot application. Some of the commonly used endpoints include: /actuator/health: Provides information about the health of your application, indicating whether it is up and running or experiencing any issues. /actuator/info: Displays general information about your application, which can be customized to include details such as version, description, and other relevant metadata. /actuator/metrics: Provides metrics about various aspects of your application, such as memory usage, CPU usage, request/response times, and more. These metrics can be helpful for monitoring and performance analysis. /actuator/env: Shows the current environment properties and their values, including configuration properties from external sources like application.properties or environment variables. /actuator/loggers: Allows you to view and modify the logging levels of your application’s loggers dynamically. This can be useful for troubleshooting and debugging purposes. /actuator/mappings: Displays a detailed mapping of all the endpoints exposed by your application, including the HTTP methods supported by each endpoint. /actuator/beans: Provides a complete list of all the Spring beans in your application, including information such as their names, types, and dependencies. Explain the Concept of Spring Boot Profiles and How They Can Be Used Spring Boot profiles provide a way to manage application configurations for different environments or deployment scenarios. With profiles, you can define different sets of configurations for development, testing, production, and any other specific environment. Here’s a brief explanation of how Spring Boot profiles work and how they can be used: Defining profiles: In a Spring Boot application, you can define profiles by creating separate properties files for each environment. For example, you can have application-dev.properties for the development environment, application-test.properties for testing and application-prod.properties for production. Activating profiles: Profiles can be activated in various ways: By setting the spring.profiles.active property in application.properties or as a command-line argument when starting the application. By using the @ActiveProfiles annotation at the class level in your tests. By using system environment variables or JVM system properties to specify the active profiles. Profile-specific configurations: Once a profile is activated, Spring Boot will load the corresponding property files and apply the configurations defined in them. For example, if the devprofile is active. Spring Boot will load the application-dev.properties file and apply the configurations defined within it. Overriding configurations: Profile-specific configurations can override the default configurations defined in application.properties or other property files. This allows you to customize certain settings specifically for each environment without modifying the core application code. Bean and component scanning: Profiles can also be used to control the bean and component scanning process. You can annotate beans or components with @Profile to specify that they should only be created and registered when a specific profile is active. Spring Boot’s default profiles: Spring Boot provides some default profiles, such as default, dev, test, and prod. The default profile is always active by default, and other profiles can be activated based on the deployment environment. What Are Some Commonly Used Annotations in Spring Boot? Here are some commonly used Spring Boot annotations: @SpringBootApplication: This annotation is used to mark the main class of a Spring Boot application. It combines three annotations: @Configuration, @EnableAutoConfiguration, and @ComponentScan. It enables auto-configuration and component scanning in your application. @RestController: This annotation is used to indicate that a class is a RESTful controller. It combines the @Controller and @ResponseBody annotations. It simplifies the development of RESTful web services by eliminating the need to annotate each method with @ResponseBody. @RequestMapping: This annotation is used to map HTTP requests to specific handler methods. It can be applied at the class level to specify a base URL for all methods within the class or at the method level to define the URL and HTTP method for a specific handler method. @Autowired: This annotation is used to automatically wire dependencies into your Spring-managed beans. It allows Spring to automatically discover and inject the required beans without the need for explicit bean configuration. @Value: This annotation is used to inject values from properties files or environment variables into Spring Beans. It can be used to inject simple values like strings or numbers, as well as complex objects. @Configuration: This annotation is used to indicate that a class provides configuration to the Spring application context. It is often used in conjunction with @Bean to define beans and other configuration elements. @ComponentScan: This annotation is used to specify the base packages to scan for Spring components, such as controllers, services, and repositories. It allows Spring to automatically discover and register these components in the application context. @EnableAutoConfiguration: This annotation enables Spring Boot’s auto-configuration mechanism, which automatically configures various components and beans based on the dependencies and the classpath. @Conditional: This annotation is used to conditionally enable or disable beans and configurations based on certain conditions. It allows you to customize the behavior of your application based on specific conditions or environment settings. Intermediate Level Spring Boot Interview Questions What Is the Use of @ConfigurationProperties Annotation? The @ConfigurationProperties annotation in Spring Boot is used to bind external configuration properties to a Java class. It provides a convenient way to map the properties defined in configuration files (such as application.properties or application.yml) to corresponding fields in a configuration class. The benefits of using @ConfigurationProperties include: Type safety: The annotation ensures that the configuration properties are bound to the appropriate types defined in the configuration class, preventing type mismatches and potential runtime errors. Property validation: You can validate the properties using various validation annotations provided by Spring, such as @NotNull, @Min, @Max, and custom validations. Hierarchical property mapping: You can define nested configuration classes to represent complex configuration structures and map them hierarchically to the corresponding properties. Easy integration: The annotated configuration class can be easily autowired and used throughout the application, simplifying the retrieval of configuration values in different components. Here’s an example of using @ConfigurationProperties: @Configuration @ConfigurationProperties(prefix = "myapp") public class MyAppConfiguration { private String name; private int port; // Getters and setters // Other custom methods or business logic } application.properties# Database Configuration spring.datasource.url=jdbc:mysql://localhost:3306/mydatabase spring.datasource.username=myusername spring.datasource.password=mypassword # Server Configuration server.port=8080 server.servlet.context-path=/myapp # Custom Application Properties myapp.name=My Application myapp.api.key=abc123 In this example, the MyAppConfiguration class is annotated with @ConfigurationProperties and specifies a prefix of “myapp”. The properties defined with the prefix “myapp” in the configuration files will be bound to the corresponding fields in this class. How Does Spring Boot Support Microservices Architecture? Spring Boot provides extensive support for building microservices-based applications. It offers a range of features and integrations that simplify the development, deployment, and management of microservices. Here’s how Spring Boot supports the microservices architecture: Spring Cloud: Spring Boot integrates seamlessly with Spring Cloud, which is a set of tools and frameworks designed to build and operate cloud-native microservices. Spring Cloud provides capabilities such as service discovery, client-side load balancing, distributed configuration management, circuit breakers, and more. Microservice design patterns: Spring Boot embraces microservice design patterns, such as the use of RESTful APIs for communication between services, stateless services for scalability, and decentralized data management. It provides a lightweight and flexible framework that enables developers to implement these patterns easily. Service registration and discovery: Spring Boot integrates with service registry and discovery tools, such as Netflix Eureka and Consul. These tools allow microservices to register themselves with the registry and discover other services dynamically. This helps in achieving service resilience, load balancing, and automatic service discovery. Externalized configuration: Spring Boot supports externalized configuration, allowing microservices to be easily configured based on the environment or specific deployment needs. It enables the separation of configuration from the code, making it easier to manage configuration properties across multiple microservices. Distributed tracing and monitoring: Spring Boot integrates with distributed tracing systems like Zipkin and Sleuth, enabling the tracing of requests across multiple microservices. It also provides integrations with monitoring tools like Prometheus and Grafana to monitor the health, performance, and resource usage of microservices. Resilience and fault tolerance: Spring Boot includes support for implementing fault-tolerant microservices using features such as circuit breakers (e.g., Netflix Hystrix), which help prevent cascading failures in distributed systems. It also provides mechanisms for handling retries, timeouts, and fallbacks in microservice interactions. Containerization and deployment: Spring Boot applications can be easily containerized using technologies like Docker, allowing for seamless deployment and scaling of microservices using container orchestration platforms like Kubernetes. What Is Spring Data? What Are Different Spring Data Starters Used in Spring Boot? Spring Data is a subproject of the Spring Framework that simplifies data access by providing a unified programming model for different data storage technologies. It reduces boilerplate code and allows developers to focus on business logic. Spring Data supports relational databases, NoSQL databases, and more. It utilizes repositories to abstract data access operations, eliminating the need for manual CRUD code. Spring Data’s starters offer pre-configured dependencies and auto-configuration for specific databases, streamlining the setup process. With Spring Data, developers can easily interact with data sources and benefit from its powerful query capabilities. Here are some examples of Spring Data starters for different types of databases: spring-boot-starter-data-jpa: This starter provides support for Java Persistence API (JPA) and Hibernate. It includes the necessary dependencies and configurations for working with relational databases using JPA. spring-boot-starter-data-mongodb: This starter provides support for MongoDB, a popular NoSQL database. It includes the necessary dependencies and configurations for working with MongoDB using Spring Data MongoDB. spring-boot-starter-data-redis: This starter provides support for Redis, an in-memory data structure store. It includes the necessary dependencies and configurations for working with Redis using Spring Data Redis. spring-boot-starter-data-cassandra: This starter provides support for Apache Cassandra, a highly scalable NoSQL database. It includes the necessary dependencies and configurations for working with Cassandra using Spring Data Cassandra. spring-boot-starter-data-elasticsearch: This starter provides support for Elasticsearch, a distributed search and analytics engine. It includes the necessary dependencies and configurations for working with Elasticsearch using Spring Data Elasticsearch. How Can You Consume RESTful Web Services in a Spring Boot Application? In a Spring Boot application, you can consume RESTful web services using RestTemplate or WebClient. RestTemplate provides a synchronous API for making HTTP requests, while WebClient offers a non-blocking and reactive approach. Both allow you to send GET, POST, PUT, DELETE requests, handle response data, and deserialize it into Java objects. How Can You Create and Run Unit Tests for a Spring Boot Application? In a Spring Boot application, you can create and run unit tests using the Spring Test framework. By leveraging annotations such as @RunWith(SpringRunner.class) and @SpringBootTest, you can initialize the application context and perform tests on beans and components. Additionally, you can use Mockito or other mocking frameworks to mock dependencies and isolate the units under test. With the help of assertions from JUnit or AssertJ, you can verify expected behavior and assertions. Finally, running the tests can be done using tools like Maven or Gradle, which execute the tests and provide reports on test results and coverage. How To Enable Debugging Log in the Spring Boot Application? To enable debugging logs in a Spring Boot application, you can set the logging.level property in the application.properties or application.yml file to “DEBUG.” This configuration will enable the logging framework to output detailed debug information. Alternatively, you can use the @Slf4j annotation on a class to enable logging for that specific class. Additionally, you can configure logging levels for specific packages or classes by setting the logging.level.{package/class} property in the configuration file. To enable debugging logs for the entire application, use:logging.level.<package-name>=DEBUG To enable debugging logs for the entire application, use:logging.level.root=DEBUG How Reactive Programming Is Supported in Spring Boot Spring Boot provides support for reactive programming through its integration with the Spring WebFlux module. It allows developers to build non-blocking, event-driven applications that can handle a high volume of concurrent requests efficiently, making use of reactive streams and the reactive programming model. How Do You Enable Security in Spring Boot Application? We can use the following different options. Using Spring Security: Enable security by adding the Spring Security Starter dependency to your project’s build configuration. OAuth2 and OpenID Connect: Enable security using OAuth2 and OpenID Connect protocols for secure authentication and authorization. LDAP Integration: Enable security by integrating with an LDAP (Lightweight Directory Access Protocol) server for user authentication and authorization. JWT (JSON Web Token) Authentication: Enable security by implementing JWT-based authentication for stateless and scalable authentication. Custom Authentication Providers: Enable security by creating custom authentication providers to handle authentication based on your own logic. What Is the Purpose of Spring Boot DevTools? How Do We Enable It? It is designed to improve productivity, streamline development workflows, and enable quick application restarts during the development phase. Here are some key features and benefits of Spring Boot DevTools: Automatic restart: DevTools monitors the classpath for any changes and automatically restarts the application when it detects modifications. Live reload: DevTools supports live reloading of static resources such as HTML, CSS, and JavaScript files. Remote application restart: DevTools provides the ability to remotely trigger a restart of the application. This can be useful when working in a distributed environment or when the application is running on a remote server. Developer-friendly error page: DevTools provides an enhanced error page that provides detailed information about exceptions and errors encountered during application development. Configuration properties support: DevTools enables hot-reloading of Spring Boot configuration properties. Changes made to application.properties or application.yml files are automatically applied without restarting the application, allowing for quick configuration updates. Database console: DevTools includes an embedded database console that provides a web-based interface to interact with the application’s database. This allows developers to easily execute SQL queries, view and modify data, and perform other database-related tasks without requiring external tools. To enable Spring Boot DevTools in your Spring Boot application, you need to include the appropriate dependencies and configurations. Here are the steps to enable DevTools: Add the DevTools Dependency: In your pom.xml file (for Maven) or build.gradle file (for Gradle), add the following dependency:<!-- Maven --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-devtools</artifactId> <scope>runtime</scope> <optional>true</optional> </dependency> // Gradle implementation 'org.springframework.boot:spring-boot-devtools' Enable Automatic Restart: By default, DevTools is enabled for applications run using the spring-boot:run command or from an IDE. However, you can also enable it for packaged applications by adding the following configuration property to your application.properties or application.ymlfile:spring.devtools.restart.enabled=true Advanced Level Spring Boot Interview Questions for Experienced Folks How Can You Enable HTTPS in a Spring Boot Application? To enable HTTPS in a Spring Boot application, you need to configure the application’s server properties and provide the necessary SSL/TLS certificates. Here are the general steps to enable HTTPS: Obtain SSL/TLS Certificates: Acquire the SSL/TLS certificates from a trusted certificate authority (CA) or generate a self-signed certificate for development/testing purposes. Configure Server Properties: Update the application.properties or application.yml file with the following server configurations: server.port=8443 server.ssl.key-store-type=PKCS12 server.ssl.key-store=classpath:keystore.p12 server.ssl.key-store-password=your_keystore_password server.ssl.key-alias=your_alias_name In the above example, replace keystore.p12 with the path to your keystore file, and set the appropriate password and alias values. Provide SSL/TLS Certificates: Place the SSL/TLS certificate file (keystore.p12) in the classpath or specify the absolute file path in the server properties. Restart the Application: Restart your Spring Boot application for the changes to take effect. After completing these steps, your Spring Boot application will be configured to use HTTPS. You can access the application using https://localhost:8443 or the appropriate hostname and port specified in the server configuration. How to Configure External Configuration in Spring Boot To configure external configuration outside the project in Spring Boot, you can use one of the following approaches: External configuration files: Instead of placing the application.properties or application.ymlfile within the project, you can specify the location of an external configuration file using the spring.config.name and spring.config.location properties. For example, you can place the configuration file in a separate directory and provide its location through the command line or environment variable. Operating system environment variables: You can leverage the environment variables provided by the operating system to configure your Spring Boot application. Define the required configuration properties as environment variables and access them in your application using the @Value annotation or the Environment object. Spring cloud config: If you have a more complex configuration setup or need centralized configuration management, you can use Spring Cloud Config. It provides a server-side component where you can store and manage configurations for multiple applications. Your Spring Boot application can then fetch its configuration from the Spring Cloud Config server. Configuration servers: Another option is to use external configuration servers like Apache ZooKeeper or HashiCorp Consul. These servers act as central repositories for configurations and can be accessed by multiple applications. How Do You Create a Spring Boot Application Using Maven? To create a Spring Boot application using Maven, follow these steps: Set Up Maven: Ensure that Maven is installed on your system. You can download Maven from the Apache Maven website and follow the installation instructions. Create a Maven Project: Open your command line or terminal and navigate to the directory where you want to create your project. Use the following Maven command to create a new project:mvn archetype:generate -DgroupId=com.example -DartifactId=my-spring-boot-app -DarchetypeArtifactId=maven-archetype-quickstart -DinteractiveMode=false This command creates a new Maven project with the specified groupId and artifactId. Adjust these values according to your project’s needs. Add Spring Boot Starter Dependency: Open the pom.xml file of your project and add the following dependency for Spring Boot:<dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter</artifactId> <version>2.5.2</version> </dependency> </dependencies> This dependency includes the necessary Spring Boot libraries for your application. Create a Spring Boot Main Class: Create a new Java class in the appropriate package of your Maven project. This class will serve as the entry point for your Spring Boot application. Here’s an example:package com.example; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; @SpringBootApplication public class MySpringBootApplication { public static void main(String[] args) { SpringApplication.run(MySpringBootApplication.class, args); } } The @SpringBootApplication annotation combines several annotations required for a basic Spring Boot application. Build and Run the Application: Use the following Maven command to build and run your Spring Boot application:mvn spring-boot:run Maven will build your project, resolve the dependencies, and start the Spring Boot application. Once the application is running, you can access it in your web browser using http://localhost:8080(by default) or the specified port if you have customized it. That’s it! You have created a Spring Boot application using Maven. You can now add additional dependencies, configure your application, and develop your desired functionality. What Are Different Types of Conditional Annotations? Some of the commonly used conditional annotations in Spring Boot are: @ConditionalOnClass: This annotation configures a bean or component if a specific class is present in the classpath. @ConditionalOnMissingClass: This annotation configures a bean or component if a specific class is not present in the classpath. @ConditionalOnBean: This annotation configures a bean or component only if another specific bean is present in the application context. @ConditionalOnMissingBean: This annotation configures a bean or component only if another specific bean is not present in the application context. @ConditionalOnProperty: This annotation configures a bean or component based on the values of specific properties in the application configuration files. @ConditionalOnExpression: This annotation configures a bean or component based on a SpEL (Spring Expression Language) expression. @ConditionalOnWebApplication: This annotation configures a bean or component only if the application is a web application. @ConditionalOnNotWebApplication: This annotation configures a bean or component only if the application is not a web application. Could You Provide an Explanation of Spring Boot Actuator Health Checks and the Process for Creating Custom Health Indicators? Health checks provide valuable information about the application’s overall health, such as database connectivity, external service availability, or any other custom checks you define. By default, Spring Boot Actuator provides a set of predefined health indicators that check the health of various components like the database, disk space, and others. However, you can also create custom health indicators to monitor specific aspects of your application. To create a custom health indicator, you need to implement the HealthIndicator interface and override the health() method. The health() method should return an instance of Health, which represents the health status of your custom component. You can use the Health class to indicate whether the component is up, down, or in an unknown state. Here’s an example of a custom health indicator:import org.springframework.boot.actuate.health.Health; import org.springframework.boot.actuate.health.HealthIndicator; import org.springframework.stereotype.Component; @Component public class CustomHealthIndicator implements HealthIndicator { @Override public Health health() { // Perform your custom health check logic here boolean isHealthy = true; // Replace with your actual health check logic if (isHealthy) { return Health.up().build(); } else { return Health.down().withDetail("CustomComponent", "Not Healthy").build(); } } } In this example, the CustomHealthIndicator class implements the HealthIndicator interface and overrides the health() method. Inside the health() method, you can write your custom health check logic. If the component is healthy, you can return Health.up(). Otherwise, you can return Health.down() along with additional details using the withDetail() method. Once you create a custom health indicator, it will be automatically detected by Spring Boot Actuator, and its health check will be exposed through the /actuator/health endpoint. How Do You Create Custom Actuator Endpoints in Spring Boot? To create custom Actuator endpoints in Spring Boot, you can follow these steps: Create a custom endpoint: Create a new class that represents your custom endpoint. This class should be annotated with @Endpoint to indicate that it is an Actuator endpoint. You can also use additional annotations like @ReadOperation, @WriteOperation, or @DeleteOperation to define the type of operations supported by your endpoint. Define endpoint operations: Inside your custom endpoint class, define the operations that your endpoint should perform. You can use annotations like @ReadOperation for read-only operations, @WriteOperation for write operations, and @DeleteOperation for delete operations. These annotations help in defining the HTTP methods and request mappings for your endpoint. Implement endpoint logic: Implement the logic for each operation of your custom endpoint. This can include retrieving information, modifying the application state, or performing any other desired actions. You have the flexibility to define the functionality based on your specific requirements. (Optional) add security configuration: If your custom endpoint requires security restrictions, you can configure it by adding security annotations or by modifying the security configuration of your application. Here’s an example of creating a custom Actuator endpoint:import org.springframework.boot.actuate.endpoint.annotation.Endpoint; import org.springframework.boot.actuate.endpoint.annotation.ReadOperation; import org.springframework.boot.actuate.endpoint.annotation.WriteOperation; import org.springframework.stereotype.Component; @Component @Endpoint(id = "customEndpoint") public class CustomEndpoint { @ReadOperation public String getInformation() { // Retrieve and return information return "This is custom endpoint information."; } @WriteOperation public void updateInformation(String newInformation) { // Update information // ... } } In this example, the CustomEndpoint class is annotated with @Endpoint to define it as an Actuator endpoint. It has two operations: getInformation() annotated with @ReadOperation for retrieving information and updateInformation() annotated with @WriteOperation for updating information. After creating your custom endpoint, it will be automatically registered with Spring Boot Actuator, and you can access it through the /actuator base path along with the endpoint ID. In this case, the custom endpoint can be accessed via /actuator/customEndpoint. How Can You Enable CORS (Cross-Origin Resource Sharing) In a Spring Boot Application? To enable Cross-Origin Resource Sharing (CORS) in a Spring Boot application, you can follow these steps: Add CORS Configuration: Create a configuration class and annotate it with @Configuration to define CORS configuration. Inside the class, create a bean of type CorsFilter to configure CORS settings.import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.web.cors.CorsConfiguration; import org.springframework.web.cors.UrlBasedCorsConfigurationSource; import org.springframework.web.filter.CorsFilter; @Configuration public class CorsConfig { @Bean public CorsFilter corsFilter() { UrlBasedCorsConfigurationSource source = new UrlBasedCorsConfigurationSource(); CorsConfiguration config = new CorsConfiguration(); // Allow requests from any origin config.addAllowedOrigin("*"); // Allow specific HTTP methods (e.g., GET, POST, PUT, DELETE) config.addAllowedMethod("*"); // Allow specific HTTP headers config.addAllowedHeader("*"); source.registerCorsConfiguration("/**", config); return new CorsFilter(source); } } In this example, we configure CORS to allow requests from any origin (*), allow all HTTP methods (*), and allow all HTTP headers (*). You can customize these settings based on your specific requirements. Enable Web MVC Configuration: If you haven’t done so already, make sure to enable Web MVC configuration in your Spring Boot application by either using the @EnableWebMvc annotation on a configuration class or extending the WebMvcConfigurerAdapter class.import org.springframework.context.annotation.Configuration; import org.springframework.web.servlet.config.annotation.EnableWebMvc; @Configuration @EnableWebMvc public class WebMvcConfig extends WebMvcConfigurerAdapter { // Additional Web MVC configuration if needed } Test CORS Configuration: Once you have enabled CORS in your Spring Boot application, you can test it by making cross-origin requests to your endpoints. Ensure that the necessary CORS headers are included in the response, such as Access-Control-Allow-Origin, Access-Control-Allow-Methods, and Access-Control-Allow-Headers. Enabling CORS allows your Spring Boot application to handle cross-origin requests and respond appropriately. It’s important to consider the security implications and configure CORS settings based on your application’s requirements and security policies. How Can You Schedule Tasks in a Spring Boot Application? In a Spring Boot application, you can schedule tasks using the @Scheduled annotation provided by Spring’s Task Scheduling feature. Here’s how you can schedule tasks in a Spring Boot application: Enable Scheduling: First, make sure that task scheduling is enabled in your Spring Boot application. This can be done by either using the @EnableScheduling annotation on a configuration class or by adding the @SpringBootApplication annotation along with @EnableScheduling on your main application class.import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.scheduling.annotation.EnableScheduling; @SpringBootApplication @EnableScheduling public class MyAppApplication { public static void main(String[] args) { SpringApplication.run(MyAppApplication.class, args); } } Copy Create Scheduled Task Method: Define a method in your application that you want to schedule. Annotate the method with @Scheduled and specify the desired scheduling expression using cron, fixed delay, or fixed rate.import org.springframework.scheduling.annotation.Scheduled; import org.springframework.stereotype.Component; @Component public class MyScheduledTasks { @Scheduled(cron = "0 0 8 * * *") // Run at 8 AM every day public void executeTask() { // Logic for the scheduled task System.out.println("Scheduled task executed!"); } } In this example, the executeTask() method is scheduled to run at 8 AM every day based on the cron expression provided. Test the Scheduled Task: Once you have defined the scheduled task, you can start your Spring Boot application and observe the scheduled task executing based on the specified schedule. The @Scheduled annotation provides several options for specifying the scheduling expression, including cron expressions, fixed delays, and fixed rates. You can choose the most appropriate option based on your scheduling requirements. How Can You Enable Caching in a Spring Boot Application? To enable caching in a Spring Boot application, you can follow these steps: Add Caching Dependencies: Ensure that the necessary caching dependencies are included in your project’s dependencies. Spring Boot provides support for various caching providers such as Ehcache, Redis, and Caffeine. Add the corresponding caching dependency to your project’s pom.xml file. For example, to use the Ehcache caching provider, add the following dependency:<dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-cache</artifactId> </dependency> <dependency> <groupId>net.sf.ehcache</groupId> <artifactId>ehcache</artifactId> </dependency> Enable Caching: To enable caching in your Spring Boot application, add the @EnableCachingannotation to your configuration class. This annotation enables Spring’s caching infrastructure and prepares the application for caching.import org.springframework.cache.annotation.EnableCaching; import org.springframework.context.annotation.Configuration; @Configuration @EnableCaching public class CachingConfig { // Additional configuration if needed } Annotate Methods for Caching: Identify the methods in your application that you want to cache and annotate them with the appropriate caching annotations. Spring Boot provides annotations such as @Cacheable, @CachePut, and @CacheEvict for caching operations. For example, suppose you have a method that retrieves data from a database, and you want to cache the results. You can annotate the method with @Cacheable and specify the cache name.import org.springframework.cache.annotation.Cacheable; import org.springframework.stereotype.Service; @Service public class DataService { @Cacheable("dataCache") public Data getData(String key) { // Logic to fetch data from a database or external service return data; } } In this example, the getData() method is annotated with @Cacheable and specifies the cache name as “dataCache”. The first time this method is called with a specific key, the data will be fetched and cached. Subsequent calls with the same key will retrieve the data from the cache instead of executing the method. Configure Cache Settings: If you need to customize the caching behavior, you can provide additional configuration properties specific to your chosen caching provider. These configuration properties can be set in the application.properties or application.yml file. For example, if you are using Ehcache, you can configure the cache settings in the ehcache.xml file and specify the location of the file in the application.properties file. By following these steps, you can enable caching in your Spring Boot application and leverage the benefits of caching to improve performance and reduce database or external service calls. Conclusion In conclusion, these Spring Boot interview questions cover a wide range of topics related to Spring Boot, its features, and best practices. By familiarizing yourself with these questions and their answers, you can better prepare for Spring Boot interviews and demonstrate your knowledge and expertise in developing Spring Boot applications.

"When hiring for DevOps engineering roles, what matters more—certifications or experience?" This question reverberates through the corridors of countless tech companies as the significance of DevOps engineering roles only grows in the evolving digital landscape. Both elements — certifications and experience — offer valuable contributions to an engineer's career. Certifications such as AWS, CKA, GCP, Azure, Docker, and Jenkins represent the structured, theoretical understanding of the technology landscape. On the other hand, experience serves as the real-world proving ground for that theoretical knowledge. But which of these two carries more weight? Here's an analysis infused with curiosity and passion, grounded in the technical and business realities of our day. The Case for Certifications Certifications provide a clear, standardized benchmark of an engineer's skill set. They attest to the individual's current knowledge of various tools, systems, and methodologies, ensuring their technical prowess aligns with industry standards. For businesses, hiring certified professionals can bring assurance of the engineer's ability to handle specific systems or technologies. This is particularly crucial in the early stages of one's career, where the lack of hands-on experience can be supplemented by formal, industry-recognized credentials. Certifications also speak to an engineer’s dedication to continuous learning — an invaluable attribute in a sector driven by relentless innovation. Furthermore, they can offer competitive advantages when dealing with clients, projecting the organization's commitment to expertise and quality. The Strength of Experience However, while certifications ensure theoretical knowledge, the chaotic, unpredictable terrain of DevOps often demands a kind of learning that only experience can provide. Real-world situations seldom stick to the script. Experience helps engineers tackle these unpredictable scenarios, providing them with a nuanced understanding that's hard to derive from certifications alone. Experience translates into tangible skills: problem-solving, strategizing, decision-making, and team collaboration — all of which are critical to managing DevOps. An experienced engineer can leverage past learnings, understanding when to apply standard procedures and when to think outside the box. The maturing engineer who has faced the heat of critical system failures or the pressure of ensuring uptime during peak loads often develops a tenacity that cannot be simulated in a testing environment. Such experiential learning is priceless and can make a marked difference in high-stakes situations. Perception and Certifications: The "Customer's" View While businesses are right to weigh the benefits of certification against experience, they must also factor in another crucial element — the perspective of "customers," who can be either paying customers in a B2B relationship or internal stakeholders from other teams or departments. Often, these "customers" feel more confident knowing that certified professionals are managing their critical infrastructure. Certifications serve as a validation of a service provider's technical skills, reassuring "customers" of the team's capability to manage complex tasks efficiently. From the "customers'" viewpoint, seeing a certified engineer indicates that the individual, and by extension, the company, has met stringent, industry-approved standards of knowledge and skills. While experience is highly valued, it is sometimes seen as more subjective and challenging to quantify, leading to "customers" placing substantial emphasis on certifications. Certification Renewals and Organizational Goals Certifications, particularly those that require renewals, ensure that engineers stay current with the evolving technology landscape. However, it's important to assess whether pursuing certification renewals aligns with the organizational goals. If a particular certification does not contribute directly to the objectives of a project or the broader organizational strategy, its renewal might not be necessary. The resources spent on such renewals might be better directed toward areas that contribute directly to the organization's mission. The Organizational Benefits of Certification Furthermore, when an organization itself earns certification, such as becoming an AWS Partner or a Kubernetes Certified Service Provider (KCSP), it opens a new realm of possibilities. These certifications not only validate the company's expertise and capabilities but also enhance its market credibility and competitive edge. As an AWS Partner, for example, companies can access a range of resources such as training, marketing support, and sales-enablement tools. They can also avail of AWS-sponsored promotional credits, allowing them to test and build solutions on AWS. Being a KCSP, on the other hand, demonstrates a firm's commitment to delivering high-quality Kubernetes services. This certification also assures "customers" that they are partnering with a knowledgeable and experienced service provider. Such partnerships and certifications can help organizations win more significant contracts, attract more clients, and also retain talented engineers seeking to work with recognized industry leaders. They demonstrate the organization's commitment to industry best practices, continual learning, and staying at the forefront of technological advancements. Bridging the Gap Certifications Experience Provide a structured, theoretical understanding of technology Provide practical, hands-on knowledge Prove an individual's skills against industry standards Offer real-world problem-solving abilities Indicate dedication to continuous learning Display adaptability and tenacity in face of real-world challenges Provide an edge in competitive scenarios Offer insights into effective team collaboration and decision-making It's crucial to remember that neither certifications nor experience can stand alone as the defining factor in DevOps engineering roles. The stage of an engineer's career and the maturity they bring to the role are products of a judicious blend of both. For those at the early stages, certifications can help them stand out and demonstrate a foundational knowledge of DevOps principles. As their career progresses, their accumulated experience, coupled with advanced certifications, exhibits a growth mindset, adaptability, and an in-depth understanding of DevOps systems and practices. Final Thoughts As we draw this discussion to a close, let's return to our initial question: "When hiring for DevOps engineering roles, what matters more — certifications or experience?" Well, we've navigated through the different stages of a DevOps engineer's career, weighed the importance of certification against the gold of experience, and taken into account the perspectives of various "customers." The conclusion is clear: it's not a case of either-or. The debate should not be about choosing one over the other, but understanding how they can symbiotically contribute to an engineer's career. Can we truly measure the importance of the structured learning that certifications offer? Can we quantify the practical wisdom that comes with experience? These are questions we may ponder, but what remains unquestionable is the unique value they both bring to the table. When we consider the perspective of the "customers", who wouldn't want the assurance that their DevOps team is armed with both certified skills and hands-on experience? And for organizations seeking to boost their reputations, why not aspire to hold industry-recognized certifications and partnerships? After all, they enhance market credibility and pave the way for bigger opportunities and promising collaborations. In conclusion, experience is an invaluable asset, a truth universally acknowledged, but the value of certifications — for individuals and businesses alike — should never be understated. Certifications and experience form a powerful combination that assures "customers," motivates teams, and drives business growth in the world of DevOps. The question then is not whether we choose between them, but how we harmoniously integrate both in our practices and operations. No matter where you stand on the spectrum of experience versus certification, remember this: they are not mutually exclusive. Both can coexist, intertwining to form a stronger, more versatile DevOps engineer. For professionals seeking to stay relevant and competitive in the fast-paced world of DevOps, the path forward is clear — embrace both theory and practice. Pursue certifications to keep up with the evolving landscape, and continually hone your skills through hands-on experience. This is the recipe for success in the thriving and dynamic field of DevOps. In the realm of DevOps, the balance between experience and certification is a delicate one, and the pendulum should never swing too far in either direction. Instead, let's allow them to work in concert, building a stronger, more comprehensive understanding of DevOps and its practices. After all, isn't that the essence of DevOps itself — bridging the gap, fostering collaboration, and creating more holistic, efficient, and powerful systems? "Knowledge comes, but wisdom lingers." — Alfred Lord Tennyson

With the development of general artificial intelligence, it is now also taking its place in jobs that require intellectual knowledge and creativity. In the realm of software development, the idea of harnessing General AI's cognitive capabilities has gained considerable attention. The notion of software that can think, learn, and adapt like a human programmer sounds enticing, promising to streamline development processes and potentially revolutionize the industry. However, beneath the surface allure lies a significant challenge: the difficulty of modifying General AI-based systems once they are deployed. General AI, also known as Artificial General Intelligence (AGI), embodies the concept of machines possessing human-like intelligence and adaptability. In the world of software development, it has the potential to automate a myriad of tasks, from coding to debugging. Nevertheless, as we delve into the promises and perils of incorporating General AI into the software development process, a series of critical concerns and challenges come to the forefront. Lack of Transparency: Its lack of transparency is at the heart of the problem with General AI in software development. Understanding how the AI arrives at decisions or solutions can be perplexing, rendering debugging, troubleshooting, or modifying its behavior a formidable task. Transparency is a cornerstone of code quality and system reliability, and the opacity of General AI presents a substantial hurdle. Rigidity in Behavior: General AI systems tend to exhibit rigidity in their behavior. They are trained on specific datasets and instructions, making them less amenable to changes in project requirements or evolving user needs. This inflexibility can lead to resistance when developers attempt to modify the AI's behavior, ultimately resulting in frustration and reduced efficiency. Over-Automation: While automation undeniably enhances software development, overreliance on General AI can lead to excessive automation. Automated systems, although consistent with their training data, may not always align with the developer's intentions. This overdependence can curtail the developer's creative problem-solving capacity and adaptability to unique project challenges. Limited Collaboration: Software development is inherently collaborative, involving multiple stakeholders such as developers, designers, and project managers. General AI systems lack the capacity for meaningful collaboration and communication, hindering the synergy achievable with human teams. This can lead to misaligned project goals and communication breakdowns. Ethical Concerns: The use of General AI in software development raises profound ethical concerns. These systems may inadvertently perpetuate biases present in their training data, resulting in biased or discriminatory software. Addressing these ethical issues is intricate and time-consuming, potentially diverting resources from development efforts. In light of these challenges and pitfalls, a human-centric approach to software development retains its essential significance. AI should be viewed as a tool that enhances and supports developers rather than replacing them entirely. Here's why this human-centric approach remains indispensable: Transparency and Control: Human developers possess the capacity to understand, control, and modify the code they create. This transparency empowers them to swiftly address issues, ensuring that software aligns with user requirements. Adaptability: Human developers can respond effectively to shifting project requirements and unexpected challenges. They can pivot, iterate, and employ creative problem-solving approaches, a flexibility that General AI may struggle to replicate due to its rigid training. Collaboration: Collaboration and communication are cornerstones of software development. Human teams can brainstorm, share ideas, and make collective decisions, fostering innovation and efficiency in ways that General AI struggles to emulate. Ethical Considerations: Human developers actively work to mitigate bias and ethical concerns in software. They can implement safeguards and engage in responsible AI practices to ensure fairness and equity in the software they create. In conclusion, while General AI holds great potential across various industries, including software development, its pitfalls and limitations must not be overlooked. Developers may encounter substantial challenges when attempting to modify General AI-based systems post-deployment, including issues related to transparency, rigid behavior, and ethical considerations. A human-centric approach that highlights the indispensable role of developers in creating, controlling, and adapting software remains paramount in addressing these challenges and delivering high-quality software products. As technology continues to evolve, striking a balance between automation and human creativity in the software development process remains a critical goal.

DZone is all about our contributors. Everyone who publishes an article here helps to make DZone the go-to resource for developers all over the world. And we’re always working to make contributing with us an even better and more rewarding experience. As part of that, today we’re announcing the next version of our Core program! Core Member Highlights From the outset, the Core program was designed to recognize the most engaged contributors who are leaders and experts in their field; those who really enjoy sharing their knowledge and are excellent writers and teachers. Here are a few examples of such contributors. Anupama Pathirage: Anupama has written a bunch of great articles for DZone as well as contributed to several of our Trend Reports. She is a highly experienced software engineer and an accomplished author and speaker. Tuhin Chattopadhyay: Dr. Chattopadhyay is a brilliant AI expert and has been hailed as one of India’s Top 10 Data Scientists. Not only that, he is an eloquent writer who excels at explaining complex concepts. He has written several articles for DZone as well as contributed to our Trend Reports and Refcards. John Vester: John is the Master Yoda of DZone. He has written over 400 (yes, you read that right) articles on DZone which have racked up nearly 29.5 million pageviews. He’s also written for countless Trend Reports and Refcardz and is one of the best writers and knowledge leaders in the industry. Upcoming Changes Until now, we haven’t had any official information on DZone.com about what the Core program is and why it’s such a cool program to be a part of. That all changes with the launch of DZone Core 2.0! We’ve added a new page to our site with a bunch of information about the Core program, with a lot more coming soon. An important thing to note is that the "requirements" aren’t hard and fast — we know you all are busy, and we still want to be sure to recognize standout contributors whenever we see them. But there are several things you can do to improve your chances of being selected: Speak at 1 DZone event per year Contribute to 2 or more Trend Reports per year Write or update 3 or more Refcards per year Accumulate 1,500 or more Reputation Points If you meet these requirements, or if you think you or anyone else would be a great fit for our Core program, you can apply here, and we’ll get back to you. There are also some great new benefits we’re adding to the Core program, including: An exclusive badge and certificate (see badge above) Priority content publication An upgraded DZone profile (coming soon) Eligibility to be selected for additional DZone programs like the Community Advisory Board and DZone Ambassador (coming soon) And much more to come Interested in the Core Program? We truly hope the “new and improved” Core program is an exciting new opportunity for all our contributors. We are so incredibly grateful for all you do for us. Will you be the next DZone Core member? If you have any questions about the Core program or about contributing to DZone, you can always reach us at community@dzone.com. Thank you! -The DZone Team

If you follow the platform engineering trend, you'll have heard people talking about paved paths and golden paths. They're sometimes used as synonyms but can also reflect different approaches. In this article, I discuss the critical difference between paved paths and golden paths in platform engineering. Paved Paths If you were a city planner designing a park, you'd need to provide areas for people to stop and routes to pass through. The perfect park is a public space you can see, stroll through, and use for recreational activities. One of the tricky parts of park design is where to place the paths. People who want to take a leisurely walk prefer a winding scenic stroll with pleasant views. But people passing through from the coffee shop to their office prefer more direct routes. Most parks offer winding trails through the park or a series of direct paths forming a giant X crisscrossing the park. As an alternative to planning the routes through the park, you can let people use it for a while. People crossing the park will wear tracks in the grass that indicate where paths may be most helpful. They literally vote with their feet. Building these paths after the demand for a route is visible means you're more likely to put them in the right place, though you can't please everyone. This approach is also dangerous, as Sam Walter Foss warned. His 1895 poem tells how a playful calf influences the design of a large city. The city's main road gets built around the trail the calf made through the woods some 300 years earlier. Paved Paths in Software You can use the paved path technique in software. You can observe how users currently achieve a goal and use what you find to generate a design for the software. Before people created software source control systems, the source wall was a common way to avoid change collisions. To create a source wall, you'd write each file name on a sticky note and add it to the wall. If you wanted to edit a file, you'd go to the source wall and find the file you wanted to change. If the sticky note was on the wall, you could take it back to your desk and make your edit. If you couldn't find the sticky note, you had to wait for its return before making your change. This manual process meant your changes would never clash, and you'd never overwrite another developer's changes. The first source control system paved this path. You'd check out a file, and the system would prevent another developer from changing it until you checked it back in. This pattern was the paved path equivalent of the source wall. If you use a modern source control system, you'll notice it doesn't work this way. That's because something better has replaced the paved path — a golden path. Golden Paths Going back to the city park example, if you had a design in mind for the use of different spaces, you might want to tempt people to take a slightly longer route that lets you make better use of the overall space. Instead of optimizing the park for commuters, you want to balance the many different uses. In this case, you'd need to find ways to attract people to your preferred route to avoid them damaging the grass and planting. In Brisbane, the park in the South Bank area features just such a path. Instead of offering an efficient straight line between common destinations, it has sweeping curves along its entire length. The path has a decorative arbor that provides shelter from the hot sun and light showers. Instead of attempting to block other routes with fences, people are attracted to the path because they can stay cool or dry. The Brisbane Grand Arbor walk is 150 meters longer than a straight-line route, but it creates spaces for restaurants, a pond, a rainforest walk, and a lagoon. Golden paths are a system-level design technique. They're informed by a deep understanding of the different purposes of the space. Golden Paths in Platform Engineering In Platform Engineering, Golden Paths are just like Brisbane's Grand Arbor. Instead of forcing developers to do things a certain way, you design the internal developer platform to attract developers by reducing their burden and removing pain points. It's the optimal space between anything goes and forced standardization. Golden paths provide a route toward alignment. Say you have 5 teams, all using different continuous integration tools. As a platform engineer, you'd work out the best way to build, test, and package all the software and provide this as a golden path. It needs to be better than what developers currently do and easy to adopt, as you can't force it on a team. The teams that adopt the golden path have an easy life as far as their continuous integration activities are concerned. Nothing makes a platform more attractive than seeing happy users. When done well, an internal development platform may feel like a paved path to the developers, but it should reduce the overall cognitive load. This often involves both consolidation and standardization. You won't solve all developer pain at once. Platform engineers will need to go and see what pain exists and think about how they might design a product that will remove it. When you start this journey, it's worth understanding the patterns and anti-patterns of platform engineering. Take the High Road World champion weightlifter Jerzy Gregorek once said: "Hard choices, easy life. Easy choices, hard life." You need to make many hard choices to create a great internal developer platform. You have to decide what problems the platform will solve and which it won't. You need to determine when a feature should flex to meet the needs of a development team and when you should let them strike out on their own path. These hard choices are the difference between a golden path and a paved path. With a paved path, you can reduce the burden on developers; the pain just moves into your platform team. A golden path will reduce the total cognitive load for everyone by dedicating the platform team to its elimination. Happy deployments!

Disclaimer: The concept of a product roadmap is vast and quite nuanced. However, this article will be focused on working with a product roadmap in Jira. As such, I am coming from an assumption that you already have a roadmap and are ready to transition your vision into Atlassian’s Project Management software. If this is not the case, please refer to this detailed guide that talks about product roadmaps in general and come back when you are ready to create a product roadmap in Jira. Using Jira for Product Roadmaps Despite Jira not being the best place to design a product roadmap, it is an excellent tool for transforming your initial plan of action into an actionable plan. Or, in simpler words, having a Roadmap in Jira makes a lot of logical sense if you are already using the app for Project Management. Why? Because all of your tasks, ergo, all of your work, are tied to the Epics in your roadmap and are clearly visible in front of the entire team. In addition to that, when you add a task to the Backlog, you’ll think about the bigger picture because you will be choosing which Epic the issue belongs to and where it lies in terms of timeframe and priorities. In addition to visibility within your own team, a roadmap in Jira has the added benefit of spreading awareness across multiple teams. For example, the work of a marketer who is dependent on the delivery of a certain feature is clearly visualized on the roadmap. That said, a roadmap in Jira is not a silver bullet. Most of its pros can become cons when viewed from a certain angle: Jira is made for internal use. As such, it is not the best choice for times when you need to communicate or share progress with the end users or stakeholders. In addition to that, there’s always the temptation of adding more and more tasks to the same Epics simply due to the fact that it is there, and you are already used to organizing your tasks within it. The best course of action would be to create a new Epics per new feature version once the existing ones are done, even when new issues are related to the same subject (for example, optimization of a web page after it has already gone live should be a separate Epic). PROS CONS Your roadmap is visible and actionable.It’s easier to review the roadmap with your team at any time.Prioritization of new work is simplified.The roadmap is used across multiple teams Not visible to end-usersNot the best tool to share with stakeholders as they’ll get an unnecessary level of detailsMisuse of a roadmap can lead to never-ending.Epics where new issues are continuously added Elements of a Roadmap in Jira Jira is a complex tool. When it comes to functionality – there’s a lot to unravel. Luckily, you’ll only really need to use three core elements of the app to transition your roadmap – Epics, Child Issues, and Dependencies. 1. Epic: An epic is a large body of work that can be divided into several smaller tasks. If, for example, you are developing an e-commerce platform, then developing a landing page would be an Epic. 2. Issue: Issues, also known as Child Issues, are the smaller tasks the Epic is composed of. If we were to follow our example with the e-commerce platform, then tasks like choosing a hosting provider, designing the page, and filling it with content would be the Child Issues. The granularity can be as broad or deep and nuanced as the project needs it to be. Smaller projects, for example, can view design as a task, while in larger teams and more complex projects, it would be an Epic with its own Child Issues. 3. Dependency: Dependencies are used to mark relationships between certain tasks and Epics. You can’t implement a payment system without deciding which one you’ll want to use. Or you can’t proceed with content unless you know the layout of the page. These are examples of a direct dependency when one Epic blocks another. How To Transfer Your Product Roadmap Into Jira As I mentioned above, Jira isn’t designed as a tool for designing product roadmaps. If you have it open – your plan should already be laid in front of you, so all that’s left to do is to transition it into the software. Let’s see an example with a company-managed iterative Jira project: navigate to your project’s Jira board and navigate to the roadmap section. This option should be visible to you in the top left corner of the sidebar. Once the roadmap interface is open – create your first epic. This option is visible at the bottom as “Sprints” and “Releases” are rows. Click on the + icon and type in the name of your Epic. Now that your first Epic is ready, you can populate it with Child Issues. Click on the + button next to the name of your epic and type in the name of your Child Issue. Once your Epics and Child Issues are all set up – click on the timeline to generate a roadmap bar. Drag the timeline sections to set up the timeframe you have estimated. Alternatively, you can set up start and due dates within the issues and epics themselves. The last thing to do would be to visualize any and all dependencies between your Epics and Child Issues. There are two ways of doing this: Drag-&-Drop: You’ll see two dots when you hover over the timeline section of the roadmap. You can grab one of the dots and drag it to the issue/epic to form a dependency. From within the issue: Alternatively, you can set the dependency from within the issue view. Open the issue and click on the button with two links. Then select the “link issue” option and link your issues. That’s it. Your roadmap is set in Jira. Best Practices for Working With Roadmaps in Jira Revise the roadmap regularly: In my team, we have a practice of revising the roadmap once a week. This helps us stay on track and clearly identify the blockers and dependencies between issues. Don’t forget to close Epics once they are actually done (for example, the e-commerce landing is live), even if some tasks are left over. Take a look at the remaining tasks during backlog refinement and either close them if they have lost relevance or shift them to the next Epic. Have your priorities straight: there are many elements that comprise the development of a successful product. You’ll need to know where your priorities are before deciding which tasks go into a certain Epic and which can be withheld until further iterations. For example, you might want to get a working website ASAP, but you can hold on to SEO optimization, which is technically also part of the development Epic. Don’t add ongoing recurring tasks to the roadmap: Continuous tasks like writing content for the blog or regular maintenance checklist shouldn’t go to the roadmap. They simply clutter your vision with tasks and Epics that don’t ever go away. Estimate and revise estimates for time-sensitive projects: This step is crucial if you have limitations on time and/or budget. Sure, estimations are not set in stone, but keeping track of your progress is still crucial. Be realistic with your scope: having too many tasks that need to be done in parallel will lead to missed deadlines and the need to reevaluate the roadmap as a whole. This is frustrating to the team. Outline your dependencies: It’s hard to prioritize work when working on a roadmap, as there’ll be too many must-do tasks. For instance, designing a landing page and running paid ads to get traffic to it are both high priorities. However, one can not be done without the other. Best Add-Ons for Working With Roadmaps in Jira When you think about it, Jira is like a Swiss army knife of project management solutions. It has a plethora of tools conveniently wrapped in one package that functions great as a whole. That said, a single blade made for cutting or a screwdriver with a nice grip will probably make that one particular job they were made for much simpler. In terms of road mapping – yes, Jira does have the tools you need, and they will get the job done. But if you need something a bit more sophisticated and laser-focused – visit Atlassian Marketplace. Structure.Gantt: This add-on gives you more control over roadmaps in Jira. The UI adds clarity by identifying critical tasks and allowing you to solve scheduling conflicts from the roadmap interface. A certain level of automation allows you to set parameters like resource capacity and automate task distribution. BigPicture Project Management & PPM: This add-on takes an interesting spin on the concept of roadmaps by visualizing them as goals rather than epics. This approach can help Agile teams who wish to break their tasks by sprints and iterations rather than by larger chunks of functionality (Epics). Smart Checklist for Jira: Smart Checklist helps you go one level deeper when filling out the roadmap with Epics and tasks. You can apply checklist templates like the Definition of Done or Definition of Ready to issues of a certain type automatically, thus making sure that once the roadmap is there – your team has the necessary steps they’ll need to follow. Conclusion If you are already using Jira and looking for an actionable roadmap designed for internal use – Atlassian has you covered. Both the out of the box functionality and the additional add-ons you can use to finetune your experience are more than enough to keep your entire team on the same page.