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Every software starts as just an idea. But how does that idea become a real, working product? The answer is the Software Development Life Cycle (SDLC). The SDLC outlines all the activities needed to take a software project from concept to delivery. Following a defined SDLC process is crucial for successful software development.

The SDLC is a framework that maps out the key phases involved in building software. It provides a structured plan of action for developers to efficiently translate requirements into a high-quality final product. The SDLC emerged in the 1960s as computing became more complex and structured programming practices were needed.

Today, following an SDLC methodology is considered an essential best practice. It enables teams to create software systematically and cost-effectively while meeting client needs. The SDLC also facilitates collaboration, quality control, and risk mitigation throughout the development process.

Without a defined SDLC approach, software projects can easily become chaotic and mismanaged. Teams waste effort on constant rework and adjustments due to lack of direction. Developers struggle with unclear requirements and make flawed assumptions.

Testing becomes an afterthought leading to excessive bugs and defects. Change requests derail schedules and budgets spiral out of control.

The SDLC acts as the overarching guide that aligns everyone on the step-by-step roadmap for the project’s lifecycle. It establishes entry and exit criteria for every phase while defining precise deliverables and approval checkpoints. This brings much-needed structure, predictability and transparency to the endeavor.

While specific SDLC models take different approaches, they all generally flow through the core phases below:

  • Planning – Define the project scope, goals, constraints, and map out a timeline.
  • Requirements – Gather detailed requirements by analyzing the problem the software will address
  • Design – Plan out the software architecture, data structures, interfaces, and technical specifications.
  • Development- Write the actual program code to build the designed software components.
  • Development- Write the actual program code to build the designed software components.
  • Testing- Rigorously evaluate the code to identify and fix defects, security vulnerabilities etc.
  • Deployment- Deliver the finalized software product for user access and production use.
  • Maintenance- Continuously monitor, update, and enhance the deployed software as needed.

These phases are executed in a specific sequential order. Each phase must be completed before fully moving to the next phase. The SDLC encourages going back to reinforce earlier phases if major changes are needed during later phases.

For instance, if critical new requirements surface during development, the process flows back to revisit the planning and design phases before implementing code changes. Similarly, unexpected issues found in testing may trigger rework of prior development activities.

This iterative aspect of the SDLC promotes a comprehensive “define before design, design before build” philosophy. It prevents blocks of effort being wasted on incorrect assumptions and rework loops.

SDLC Phases in Detail

Now let’s take a closer look at each of the key SDLC phases and the typical activities involved:

Planning Phase

This initial phase shapes the whole software project. The main objective is to establish a clear plan for executing the remaining lifecycle phases. Core planning activities include:

  • Gathering basic project requirements and scope
  • Conducting feasibility studies to evaluate alignment with business objectives
  • Determining needed resources like budget, team structure, tools
  • Creating timelines and mapping out deliverable milestones
  • Defining project success criteria and mechanisms for tracking progress

Careful planning upfront ensures the actual development work proceeds smoothly. It lays the foundation by securing stakeholder approval, acquiring necessary resources and establishing governance controls.

The planning phase also drives out any potential project risks or uncertainties early on. For instance, the team may identify that the software requires integration with a legacy system currently undocumented. This risk can be mitigated by adding a separate track to investigate that system’s architecture.

With a solid project plan established, the realistic scope and constraints are clear. This prevents unrealistic expectations that could derail the project down the line.

Requirements Phase

You can’t build effective software without first understanding the real problem it needs to solve. The requirements phase is all about pinpointing the specific requirements the solution must meet. Key activities are:

  • Comprehensively analyzing the needs and expectations of end-users/customers
  • Consulting subject matter experts to fully understand requirements
  • Documenting functional specifications and technical capabilities required
  • Creating use cases and diagrams to model requirements
  • Prioritizing requirements based on factors like complexity and business value
  • Getting customer/stakeholder approval on final documented requirements

Requirements are typically documented in a Software Requirements Specification (SRS) artefact. This becomes the official source of truth that guides all subsequent development.

During this phase, business analysts and technical leads work closely with end-user representatives and domain experts. They translate high-level requirements into clear, specific, and measurable statements. Responsibilities, assumptions, constraints and acceptance criteria are all captured meticulously.

Comprehensive requirements elicitation is crucial to avoid costly rework down the line. With precise requirements defined, the project is ready to progress into design.

Design Phase

This phase translates the documented requirements into technical specifications for actually building the software. The design process involves:

  • Creating high-level architectural plans for components like databases
  • Designing low-level logic for data flows, algorithms, interface mock-ups etc.
  • Selecting appropriate programming languages, platforms and frameworks
  • Planning data storage solutions, network infrastructures and integrations
  • Designing for key non-functional aspects like security, performance and scalability
  • Mapping dependencies between different software components
  • Prototyping to validate the proposed designs with stakeholders

The design lays the critical technical foundation before coding begins. Software architects and leads make key technology and implementation choices during this stage based on the specified requirements. They create detailed component specifications, interface definitions, data models and more

To visualize the end-solution better, teams may build prototypes during design. These are simplified, inexpensive models of the actual software that can be reviewed and iterated on with customers for feedback. Prototypes validate that the design meets users’ expectations before investing in full development.

Seasoned designers also incorporate industry best practices, coding standards and test-driven approaches into their design specifications. This fosters high internal software quality and long-term maintainability from the ground up.

Development Phase

With requirements and designs finalized, the real programming starts in this phase. Typical development activities encompass:

  • Writing clean, well-documented program code aligned with designs
  • Managing code repositories and updates from multiple developers
  • Integrating different software modules and components
  • Creating databases, data flows and application interfaces per design
  • Setting up test environments and implementing DevOps practices
  • Conducting initial unit testing of individual code components

Code development continues in an incremental, iterative cycle until the full software is built. The entire development process closely follows the guidelines established during the design phase.

In modern development environments, elaborate tooling is set up to streamline this phase. Version control systems manage code check-in/check-outs across globally distributed teams. Automated builds and continuous integration servers periodically compile code and run unit tests to detect errors quickly.

With the rise of Agile practices, development also occurs in short cycles called Sprints or Iterations. Each cycle delivers an executable prototype for customer review and course correction. This adaptive “build-measure-learn” feedback loop is invaluable to building the right product.

Throughout this phase, thorough documentation and knowledge sharing is vital. Teams maintain technical guides, architecture diagrams, data dictionaries etc. for smooth knowledge transfer.

Testing Phase

No software is usable until it has been rigorously tested and proven to work correctly. The testing phase focuses on:

  • Defining test strategies, scenarios, cases and scripts
  • Testing individual modules through unit tests
  • Performing integration tests on integrated components
  • Conducting system tests on the entire assembled application
  • Checking for performance, load, stress and security holes
  • User acceptance testing to validate requirements fulfillment
  • Reporting, fixing and re-testing identified defects

Exhaustive testing aims to deliver a high-quality, defect-free product. During this stage, a dedicated team of quality assurance specialists gets involved. Using both manual and automated techniques, they meticulously evaluate every aspect of the developed software.

Unit tests focus on verifying the correct functionality of individual code modules like classes or functions. Integration tests check for defects when these units are combined and interfaced together. System testing is an end-to-end validation of the entire integrated application per specified requirements

Additionally, rigorous non-functional testing validates factors like application performance under load, usability, compliance, security and data integrity. Tools that simulate realistic user traffic are leveraged for effective performance engineering.

Testing isn’t just a one-time event – it occurs continuously through the software lifecycle. As new defects are discovered and fixed in each phase, regression tests are performed to verify the integrity of already tested components. Every change implemented triggers its own targeted testing cycle.

For complex business applications, user acceptance testing is essential. Here, actual end-users or subject matter experts evaluate the software against their stated requirements. Their signoff is a mandatory release criteria before deployment can proceed.

With the software successfully tested, it’s ready for deployment into production environments. Deployment includes:

  • Final data migration and security checks before going live
  • Launching the software for customer/end-user access
  • Providing documentation, guides and user training
  • Transferring project deliverables and knowledge to operations teams
  • Continually monitoring for initial operational issues

Getting customer/user feedback in this phase is crucial for future enhancements. Even after rigorous testing, real-world usage uncovers unique scenarios that couldn’t be replicated in controlled environments. Monitoring tools track application logs and behaviors to quickly isolate issues.

In modern DevOps practices, deployment itself is increasingly automated using advanced release management tooling. Rather than risky “big-bang” releases, incremental updates are pushed to production on a frequent cadence using continuous delivery pipelines. This minimizes downtime while accelerating feedback cycles.

Techniques like blue/green or canary deployments are adopted for applications with stringent service uptime requirements. New code is deployed to parallel production-like environments for comprehensive testing before being rolled out. Simple rollbacks are possible if any critical issues arise without downtime.

Deployment planning also covers rollout logistics – scheduling communication, enabling service transitions, migrating data from legacy systems and more. Providing user training and documentation is essential for customer adoption.

The deployment milestone officially transfers project ownership from the delivery/development teams to support and operations. However, substantial knowledge transfer effort is required for a smooth handover.

Maintenance Phase

The SDLC never truly ends with deployment. Ongoing maintenance activities are essential, such as:

  • Tracking issues and fixing defects discovered in production
  • Implementing approved changes and software upgrades
  • Applying security patches and updates to third-party integrations
  • Performance tuning, scaling infrastructure and optimizations
  • Evolving features based on shifting requirements
  • Migrating to new technologies/frameworks as they emerge

The maintenance phase can stretch for the entire productive lifetime of the software. It involves constantly nurturing the deployed application to sustain optimal operation, security and user satisfaction.

Changes of all types – basic bug fixes, major feature enhancements, infrastructure upgrades – all follow a miniature SDLC cycle of their own. They are first analyzed, documented, designed, developed, tested and promoted through different environment pipelines before production deployment.

Effective maintenance requires meticulous application logging, monitoring and support workflows. Dedicated resources are allocated for ongoing operations – technical support, sustenance engineering, environment management and more. Any downtime or operational disruptions are carefully analyzed and preventive measures put in place.

While the SDLC requires upfront effort, sticking to a proven development methodology pays off tremendously:

  • Better Quality – The phased approach encourages quality at every step through thorough planning, design reviews, testing and controlled releases. Defects are identified earlier when they’re easier to fix.
  • Faster Delivery – The structure helps achieve faster time-to-market without compromising on scope. Clear requirements lead to efficient downstream phases.
  • Cost Savings – Early risk identification and mitigation reduce expensive rework and delays. The SDLC lifecycle optimizes resource allocation across phases.
  • Clearer Communication – Comprehensive documentation and phase approvals keep stakeholders aligned and informed throughout development.
  • Easier Maintenance – Well-structured code, test scripts and supporting artifacts streamline long-term maintainability post-deployment.

Overall, the SDLC drives better software solutions that meet customer needs within constraints. It establishes accountability and control over the entire process.

Beyond these direct benefits, adopting SDLC practices promotes organizational maturity in various areas:

Process Standardization

The defined entry/exit criteria for each SDLC phase and associating deliverables bring standardization across all projects. Teams follow consistent, repeatable processes rather than ad-hoc methodologies that vary between initiatives.

This standardization facilitates simpler process measurement and improvement over time. For instance, by analyzing requirements churn and resulting defects across projects, teams can optimize requirements practices. Or by tracking unit test coverage, they can improve code quality practices.

Clear Roles and Responsibilities

Every phase has well-defined roles like business analysts, architects, developers, testers etc. Mapping those roles to teams/individuals brings accountability and ownership to the process.

Rather than resources frequently context-switching between multiple activities, they can focus on their primary responsibilities. For instance, developers won’t have to split their time defining requirements or configuring test environments.

At the same time, SDLC processes make role responsibilities very explicit. There is little ambiguity over who needs to contribute what in each phase. Role boundaries and handoffs are crystal clear.

Stakeholder Collaboration

The SDLC establishes a cadence for engaging customers and other stakeholders. Phase approvals and sign-offs mandate their involvement at key intervals for reviewing requirements, designs, prototypes and more.

This regular collaboration fosters transparency and trust in the process. Rather than periodic surprise status updates, stakeholders stay in the loop continuously. Both customers and teams are on the same page regarding expectations, timelines, and quality targets.

Enhanced Visibility and Control

The SDLC provides a structured master framework for managing the complexities of software development. Detailed status reporting on progress and quality is possible at every phase through defined milestones and checkpoints.

Management has greater visibility and control through real-time metrics like requirements traceability, design defects, code coverage, test results and release dashboards. This insight allows for identifying gaps, bottlenecks, and risks so corrective actions can be taken proactively.

For regulated domains like finance or healthcare, auditing adherence to SDLC processes is critical for compliance. The comprehensive documentation and phase artifacts simplify demonstrating conformance.

While these governance and quality facets provide immense value, their implementation needs to be balanced without becoming overly bureaucratic. Too many rigid processes and approval layers can hinder development agility and speed.

Popular SDLC Models

There are various SDLC models that prescribe different process flows and philosophies. Let’s review some of the most widely adopted:

Waterfall Model

The waterfall is one of the earliest and most traditional SDLC approaches. It takes a simple, linear approach where each phase begins only after the previous one finishes. Development flows sequentially through the phases:

Requirements -> Design -> Implementation -> Verification -> Maintenance

The waterfall model advocates meticulous upfront planning and documentation during the initial phases. Its rigid, phase-gated structure aims for predictable execution with little uncertainty.

Once the project moves past the planning and design phases, those baselines essentially get “locked down”. Any changes to requirements or designs in later phases requires expensive rework from the beginning. As such, the waterfall relies heavily on establishing a comprehensive understanding of software needs upfront.

The strengths of the waterfall include its straightforward, easy-to-understand approach with distinct roles and deliverables. The sequential phases allow for careful documentation, verification activities, and sign-offs. Progress tracking is simple since tasks are linearly ordered.

However, the waterfall is very inflexible to incorporate changes in requirements or design once the phases progress. It follows a strict “define everything upfront” approach which is increasingly unrealistic for modern software. Projects must have extremely comprehensive upfront requirements for the waterfall to be viable.

The model provides little opportunity for early customer feedback and course correction. Applications are only reviewed in their entirety during the late testing phases – leading to excessive and costly rework if issues are discovered. Incremental development and faster feedback cycles are not possible.

The waterfall works best for smaller, well-defined projects with minimal uncertainty around requirements. However, it responds poorly to rapidly changing environments and disruptive markets where continuous adaptation is essential.

Agile Model

On the opposite end is the Agile model centered around iterative, incremental development. Rather than a single, prolonged process, it breaks projects into a series of short cycles called Sprints or Iterations.

Each Sprint includes all core activities like planning, coding, testing, and reviews. At the end, working software representing a vertical slice of requirements is delivered. Successive Sprints keep enhancing the working prototype until all planned functionality is implemented. Constant customer collaboration and responding to change are core Agile tenets.

Within each Sprint, activities like requirements analysis, design, coding and testing are time-boxed into a 2-4 week cycle. Teams frequently re-plan and re-prioritize their backlog of work every iteration based on latest feedback and learnings.

Agile emphasizes face-to-face communication, collaborative decision-making, and self-organizing teams over rigid processes. Progress is tracked via working software released every couple of weeks rather than volumes of documentation.

This methodology works great for projects with dynamic, evolving requirements. Its adaptive nature supports rapidly delivering high-priority features to customers for quick feedback. Incremental development mitigates risks around requirement changes and bugs are caught earlier.

On the flipside, Agile requires highly skilled cross-functional teams proficient across the entire SDLC. There is little upfront detailed planning – requirements emerge and design occurs progressively through each iteration.

This “learn as you go” model risks inconsistencies and architectural challenges if not actively refactored. It can lead to technical debt accumulation without conscious effort.

Agile also relies heavily on continuous customer involvement and responsiveness for feedback, which may not always be feasible. Finally, its lack of emphasis on extensive documentation can hinder knowledge transfer for long-running projects.

Iterative Model

The Iterative model shares the cyclical philosophy of Agile but incorporates more structure. The overall software is built across multiple iterations, with each iteration passing through the SDLC phases.

Each iteration progressively extends the executed functionality until the final system is complete. The initial iteration creates a minimal core product, while later iterations iteratively enhance it.

For instance, let’s say we’re building a cloud file storage application. The first iteration delivers the core file upload/download functionality on web and mobile. Subsequent iterations extend it with sharing, version control, audit logs etc.

Within each iteration, the entire SDLC sequence of planning, requirements, design, coding and testing is followed end-to-end. However, rather than monolithic upfront design, the specifications evolve incrementally based on learnings from prior iterations.

This model allows developers to take an incremental “grow” approach to software construction. Early working prototypes improve predictability and risk management compared to one big development cycle.

The Iterative SDLC encourages robust engineering practices like refactoring and continuous integration that Agile may lack. At the same time, it offers more flexibility than the rigid Waterfall model to adapt to changing requirements.

However, determining what should go into each iteration, and getting the incrementing functionality complete and fully integrated remains a challenge. The model lacks specific best practices around feedback loops and customer collaboration like Agile.

Spiral Model

The spiral SDLC model focuses heavily on risk management and customer validation. The lifecycle progresses through a spiral of four main quadrants:

  • Establish goals based on business requirements
  • Evaluate alternatives and assess risks
  • Develop and test the next working iteration
  • Obtain user feedback and plan for the next iteration

For each spiral iteration, the quadrant activities provide a controllable degree of risk assessment and resolution. Based on the risk exposure, the iteration is either terminated or proceeded for the next spiral path.

High-risk projects initiate the spiral model with a prototype iteration that explores key risks. Once risks are understood and mitigated, development can progress through subsequent spiral iterations with greater confidence.

The evaluation quadrant in each spiral is the driving force. It examines risks like performance, functionality, cost, quality and timeline. Mitigation plans are charted out along with defining what portion of the project will be handled in the next iteration.

For instance, a complex tax software product may tackle critical security and regulatory compliance risks first through spikes and prototypes. Once those risks are resolved, full-fledged development can commence.

The spiral model is ideal for large, complex projects with ambiguous initial requirements. Its emphasis on risk mitigation upfront fosters controlled execution within broad constraints.

However, evaluation and risk management activities add overheads in terms of time, resources and costs. The model lacks specific guidelines for transitioning between spiral levels. It may not be suitable for smaller, more straightforward software with lower risk profiles.

V-Shaped Model

The V-Shaped model maps testing activities to complement the development phases in a V-configuration. For instance, system testing traces back to requirements gathering.

Each verification/validation phase directly associates with its corresponding development phase on the opposite “V” side. This bi-directional traceability ensures complete test coverage and sign-offs for each stage.

Each verification/validation phase directly associates with its corresponding development phase on the opposite “V” side. This bi-directional traceability ensures complete test coverage and sign-offs for each stage.

For example, unit test cases are designed during the coding stage. As developers complete code modules, the associated unit test cases are executed. Test results are reviewed and signed off before integration with other modules.

Similarly, system tests are derived from system specifications during the high-level design phase itself. This details “what” the complete system should do rather than “how” each functionality will be implemented.

The V-shaped structure encourages test-driven development principles and defects get spotted early before progressing too far down the lifecycle. However, its sequential implementation can limit flexibility to change or adapt.

Production environments are only configured during the last leg of the deployment phase. There is limited scope of prototyping or quick user feedback cycles until very late in the process.

The V-Model combines beneficial aspects of the Waterfall’s discipline with the validation emphasis of Iterative development. For evolutionary or incremental projects, it can be wrapped inside a higher-level cycle to provide best of both worlds.

SDLC Tools and Technologies

To implement various SDLC models effectively, teams leverage dedicated SDLC tools and technologies. Here are some popular categories:

  • Requirements Management Tools
    Help gather, organize, prioritize and document software requirements. Examples: Jira, Confluence, VersionOne
  • Design/Modeling Tools
    Enable architects and designers to model software components using standardized notations. Examples: Rational Rose, Microsoft Visio, Creately
  • Programming IDEs/Editors
    Integrated Development Environments (IDEs) with code editors, debuggers, compilers etc. Examples: Visual Studio, Eclipse, IntelliJ
  • Source Code Management
    Version control systems to manage and track code changes from multiple developers. Examples: Git, Subversion, Mercurial
  • Automated Testing Frameworks

    Allow creating automated test scripts to continuously validate software quality. Examples: Selenium, JUnit, TestNG
  • CI/CD Tools

    Continuous Integration (CI) and Continuous Delivery (CD) tools to streamline build, test and deploy cycles. Examples: Jenkins, CircleCI, Travis CI
  • Project Management Software

    Help plan projects, distribute tasks, track schedules, share updates, documents etc. Examples: JIRA, Trello, Microsoft Project

Selecting appropriate SDLC toolset automates many manual, error-prone activities. It ensures efficient collaboration while maintaining process rigor. Having the right tools integrated across phases provides complete traceability from requirements to released software.

Modern agile teams are increasingly leveraging comprehensive toolchains that integrate across SDLC tools. For instance, pulling requirements directly into development tools, automating test creation from designs, and kicking off CI/CD pipelines on code commits.

Adopting effective tooling goes a long way in optimizing SDLC activities and instilling best practices. However, it’s still critical to have defined SDLC processes in place that the tools enable rather than the other way around.

With several proven SDLC models available, how do you determine the most suitable choice for your project? Here are some key factors to consider:

  • Project Scope and Complexity

    Models like Waterfall work best for smaller, straightforward projects with well-defined requirements upfront. More complex endeavors with uncertain or evolving needs are better handled by Agile, Iterative or Spiral models.
  • Team Maturity and Skills

    If you have junior teams more comfortable with structured approaches, Waterfall or V-Shaped may be preferable. Seasoned agile professionals can leverage flexible models like Scrum or XP.
  • Customer Involvement

    Customers that want a high level of involvement are ideal for collaborative Agile practices. But if they simply want to set requirements and review periodically, sequential models are suitable.
  • Project Criticality

    Mission-critical projects with minimal risk tolerance should consider highly disciplined models like Spiral for comprehensive planning and stage verification. Proof-of-concept apps allow more relaxed approaches.
  • Project Criticality

    Mission-critical projects with minimal risk tolerance should consider highly disciplined models like Spiral for comprehensive planning and stage verification. Proof-of-concept apps allow more relaxed approaches.
  • Product Characteristics

    Models like Iterative or Incremental are great for products with a high degree of design uncertainty. But well-understood domains can fit methodologies like Waterfall or V-Shaped.
  • Timeframe and Budget
    If your timeline is relatively fixed with fewer financial constraints, Agile frameworks allow flexibility to evolve scope. For stricter budgets and deadlines, a plan-driven Waterfall cycle is prudent.
  • Organizational Culture
    Does your environment embrace collaborative decision making? Agile may be a smooth fit. Or do you have rigid hierarchies preferring top-down execution? Models like Waterfall align better initially.

Ultimately, seasoned development teams will likely use hybrid variations tailored to their unique contexts rather than adhering strictly to one model. Project characteristics may necessitate plan-driven approaches for some components and agile delivery for others.

The organization’s overarching business drivers and strategic priorities should guide the model selection. Stable products focused on efficiency and cost control tend towards traditional methodologies. While innovation-driven initiatives are better served by embracing adaptive models.

Whatever methodology is chosen, the important factor is sticking to a defined SDLC process rather than an ad-hoc disorganized approach. Adopting an SDLC model is just the starting point – comprehensive planning, rigorous execution and continuous process improvement is essential for success.

Conclusion

We’ve established that systematically following an SDLC process is a software development best practice. Doing so provides tremendous value through better quality, predictability, stakeholder visibility and overall project control.

That said, simply adopting an SDLC isn’t a silver bullet – effective execution is pivotal. Teams must understand the model nuances and continuously monitor, inspect and adapt as needed. Strong communication, documentation discipline and customer collaboration are critical.

Most importantly, your chosen SDLC should align with your specific product vision, timelines and organizational culture. With judicious application of the right model, principles and tools, the SDLC paves the way for smooth software delivery. So whatever your software needs, embrace a strategic SDLC approach from the get-go. Make that core investment to reap the multitude of long-term payoffs and efficiency.

We’ve established that systematically following an SDLC process is a software development best practice. Doing so provides tremendous value through better quality, predictability, stakeholder visibility and overall project control.

That said, simply adopting an SDLC isn’t a silver bullet – effective execution is pivotal. Teams must understand the model nuances and continuously monitor, inspect and adapt as needed. Strong communication, documentation discipline and customer collaboration are critical.

Most importantly, your chosen SDLC should align with your specific product vision, timelines and organizational culture. With judicious application of the right model, principles and tools, the SDLC paves the way for smooth software delivery. So whatever your software needs, embrace a strategic SDLC approach from the get-go. Make that core investment to reap the multitude of long-term payoffs and efficiency.

TAV Tech Solutions is a leading software development company specializing in offering a complete range of software service and technology solutions across industry verticals. Software development cycle is a critical aspect of modern day businesses. TAV Tech Solutions guide businesses in turning their ideas into a functional and operational structure. Besides they help in streamlining development processes, user experience, manage changes, and related steps in SDLC.

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