SDLC defined

SDLC stands for software development lifecycle. It’s a ubiquitous framework for managing an entire software project from start to finish, ensuring consistency, efficiency, and quality. Typically, the SDLC has several key stages, including requirements gathering and analysis, system design, coding, testing, deployment, and maintenance and support. Ideally, this is a circular process that eventually feeds back into the initial planning stage, ensuring that the software continues to evolve and improve.

The SDLC development helps development teams efficiently build high-quality software at a lower cost while ensuring that it meets stakeholder requirements and adheres to their organization’s standards for quality, security, and compliance. Today's teams are increasingly leveraging AI tools to enhance efficiency and quality throughout the entire SDLC.

The Software Development Lifecycle phases and how they work

Each phase of the SDLC has key activities designed to drive efficiently, quality, and customer satisfaction. This step-by-step process gives structure to not only software development teams, but also product managers, user experience (UX) designers, and quality assurance teams.

Phase 1: Requirements gathering and analysis

Accurate, complete, and measurable user requirements are the foundation for any successful SDLC project to ensure that the software will meet the needs and expectations of its users. These requirements define what the software must do from the user's perspective, such as allowing customers to search for products, enabling users to create accounts and log in, or providing administrators with the ability to generate reports and manage permissions. It's an essential step that helps avoid costly rework and project delays.

In this phase, product and engineering managers work together to:

• Gather requirements by conducting interviews, holding workshops or focus groups, preparing surveys or questionnaires, and observing how stakeholders work.

• Evaluate the requirements as they relate to system feasibility, and software design and testing.

• Model the requirements and record them in a single document, such as a user story, software requirements specification, use case document, or process specification.

The role of AI: AI can streamline this phase by analyzing user feedback, extracting insights from large datasets, and identifying patterns in requirements that might be missed through manual analysis.

Phase 2: System design

Effective system design properly accounts for all documented requirements. In this phase, software architects—technical experts who design the overall structure and framework of software systems—use tools to visualize information about the application’s behavior and structure, including:

• The unified modeling language (UML) to illustrate the software’s architectural blueprints in a diagram.

• Data flow diagrams to visualize system requirements.

• Decision trees and decision tables to help explain complex relationships.

• Simulations to predict how the software will perform.

To support the distinct layers within a software application, software architects use a design principle called separation of concerns, which involves organizing code so that each part handles a specific, well-defined responsibility. A software program that's designed to align with the separation of concerns principle is called a modular program.

Modular software design separates program functionality into interchangeable, independent modules, so that each module contains everything it needs to execute one aspect of the software's functionality. This approach makes it easier to understand, test, maintain, reuse, scale, and refactor code. It’s a more effective approach than monolithic software design, which combines all functionality into a single, tightly coupled structure, which can make the codebase harder to modify, debug, and scale as the application grows in complexity.

The role of AI: AI is transforming system design by generating architectural diagrams, suggesting optimal design patterns based on requirements, and predicting potential bottlenecks or scalability issues before implementation begins.

Phase 3: Coding

In the coding phase, developers translate the system design specifications into actual code. It’s critical that developers follow best practices for writing clean, maintainable, and efficient code, including:

• Writing code that’s easy to understand and read.

• Using comments to explain what the code does.

• Using version control to track any changes to the codebase.

• Refactoring the code if needed.

• Conducting a code review when coding is completed to get a second opinion on the code.

• Providing code documentation that explains how the code works.

The role of AI: AI-powered coding assistants are revolutionizing this phase by providing real-time code suggestions, automatically generating boilerplate code, and helping developers write more efficient, bug-free code faster than ever before.

Phase 4: Testing

Before it’s released to production, the software is thoroughly tested for defects and errors. High-quality code is not only essential for effective and successful software, but also for identifying any security concerns.

• The software test plan provides critical information about the testing process, including the strategy, objectives, required resources, deliverables, and criteria for exit or suspension.

• Test case design establishes the criteria for determining if the software is working correctly or not.

• Test execution is the process of running the test to identify any bugs or software defects.

Security testing is a critical component that identifies vulnerabilities, authentication flaws, and data protection issues. Teams conduct penetration testing, vulnerability scanning, and security code reviews to ensure the software can withstand potential threats and protect sensitive user data.

Developers and quality assurance teams use automated testing tools to quickly test software, prepare defect reports, and compare testing results with expected outcomes. Automated testing saves time and money, provides immediate feedback, and helps improve software quality. Automated testing can be used for:

• Unit testing: Developers test the individual software modules to validate that each one is working correctly.

• Integration testing: Developers test how the different modules interact with each other to verify that they work together correctly.

• System testing: Developers test the software to verify that it meets the requirements and works correctly in the production environment.

• User acceptance testing: Stakeholders and users test the software to verify and accept it before it’s deployed to production.

The role of AI: AI enhances testing by automatically generating test cases, predicting areas of code most likely to contain bugs, and intelligently prioritizing which tests to run based on code changes and historical data.

Phase 5: Deployment

There are three main phases to deploying software in a production environment:

• The development team commits the code to a software repository.

• The deployment automation tool triggers a series of tests.

• The software is deployed to production and made available to users.

Teams can choose from several deployment strategies depending on their risk tolerance and infrastructure. Blue/green deployments maintain two identical production environments to enable instant rollbacks, while canary deployments gradually release new features to a small subset of users before full rollout. Rolling deployments update applications incrementally across servers to minimize downtime.

Effective software installation requires a consistent deployment mechanism and a simple installation structure with minimal file distribution. The team must also make sure that the correct configuration file is copied to the production environment and that the correct network protocols are in place. Before migrating data to the new system, the team also needs to audit the source data and resolve any issues.

Release management makes software deployment smooth and stable. This process is used to plan, design, schedule, test, and deploy the release. Versioning helps ensure the integrity of the production environment when upgrades are deployed.

The role of AI: AI is improving deployment processes by predicting optimal deployment timing, automatically monitoring system performance during rollouts, and intelligently routing traffic to minimize user impact during updates.

Phase 6: Maintenance and support

After the software is deployed, the software maintenance lifecycle begins. Software requires ongoing maintenance to ensure that it operates at peak performance. Developers periodically issue software patches to fix bugs in the software and resolve any security issues.

Maintenance activities also include performance monitoring of both the software’s technical performance and how users perceive its performance. Providing training and documentation to users, along with addressing user issues and upgrading their systems to make sure they’re compatible with the new software, are also key components of the software maintenance lifecycle.

Ideally, once the maintenance lifecycle is well under way, this process starts all over again. By circling back to the first phase, development teams continue to evolve and improve the product.

The role of AI: AI accelerates maintenance by automatically detecting performance anomalies, predicting when systems might fail, and even suggesting or implementing fixes for common issues without human intervention.

SDLC methodologies

In the world of software development, different methodologies serve as structured approaches to guide the process of creating and delivering software. These methodologies shape how teams plan, execute, and manage their projects, impacting factors such as flexibility, collaboration, and efficiency. Let's take a look at some of the more prominent SDLC methodologies.

Agile methodology

Agile emphasizes iterative development, collaboration, and rapid response to change. Teams work in short sprints to deliver working software frequently while adapting to evolving requirements and customer feedback. This methodology is well-suited for complex projects that require flexibility and collaboration between many different teams.

Agile emphasizes iterative development, collaboration, and rapid response to change. Born from the Agile Manifesto in 2001, it revolutionized software development and is still widely used today. Teams work in short sprints—typically 1-4 week cycles—to deliver working software frequently while adapting to evolving requirements and customer feedback. This methodology is well-suited for complex projects that require flexibility and collaboration between many different teams.

Learn more about agile methodology.

Iterative methodology

Iterative development builds software through repeated cycles of planning, development, and evaluation. Each iteration produces a working version that's refined based on feedback and lessons learned from previous cycles. This methodology has a bit of overlap with the agile methodology but best suits projects where requirements may evolve but a structured approach is still needed, specifically large projects that can be divided into smaller pieces.

Spiral methodology

Spiral combines iterative development with systematic risk analysis at each phase. Teams repeatedly cycle through planning, risk assessment, development, and evaluation while gradually building toward the final product. This model is best for large, complex projects with significant technical or market risks.

V-shaped methodology

V-shaped emphasizes testing and validation in a sequential process. This model extends waterfall by pairing each development phase with a corresponding testing phase. Design phases on the left side of the "V" directly inform testing phases on the right side. This approach works well for projects where quality assurance and testing are critical requirements.

Lean methodology

Lean development focuses on eliminating waste, optimizing efficiency, and delivering value quickly. Teams emphasize continuous improvement, just-in-time decision making, and empowering developers to make decisions. This methodology is ideal for projects where achieving short-term goals is a priority and when there’s frequent interaction between the development team and users.

How to choose an SDLC methodology

There's no one-size-fits-all solution when it comes to selecting an SDLC methodology. Every model has distinct pros and cons that you need to factor into your decision. The key is understanding your specific context:

• What type of product are you building?

• How experienced is your team?

• What are your organization's constraints and culture?

Don’t forget to consider factors like project complexity, timeline flexibility, stakeholder involvement, and risk tolerance. The right methodology aligns with both your team's strengths and your project's unique requirements.

Benefits of the software development lifecycle

A well-implemented SDLC transforms how your team builds software, delivering measurable improvements in quality, efficiency, and predictability. When teams follow structured processes, they catch bugs earlier, reduce costly rework, and ship more reliable products. The benefits extend beyond technical outcomes to include:

• Improved collaboration between developers, stakeholders, and operations teams.

• Better visibility into project progress and potential roadblocks.

• Reduced time-to-market through streamlined development workflows.

• Higher code quality and fewer production issues.

• More accurate project estimates and resource planning.

Challenges of the software development lifecycle

The biggest challenges to a successful SDLC often stem from inadequate communication, planning, testing, or documentation. Best practices to address these issues include:

• Collaboration between the development, IT operations, security, and product teams, and stakeholders.

• Clearly defining user requirements and project deliverables, timelines, and milestones.

• Detailed documentation of resources, schedules, code, and other deliverables.

• Frequent meetings to identify and resolve issues as a team.

• Retrospectives to drive continuous improvement across the SDLC.

How does the SDLC address security?

Due to increasing cyberattacks and security breaches, development teams are under pressure to improve application security. SDLC security is a set of processes that incorporate robust security measures and testing into the SDLC. Best practices support the detection and remediation of security issues early in the lifecycle—before the software is deployed to production.

Some teams are using development platforms that build security analysis into their workflow. For example, the GitHub platform scans code for security issues as it's written in the coding phase. AI is further enhancing security by automatically identifying vulnerabilities, suggesting fixes, and continuously monitoring for emerging threats.

How can DevOps be integrated into the SDLC?

DevOps is an approach to SDLC that combines development (dev) and operations (ops) to speed the delivery of quality software. The core principles of this approach are automation, security, and continuous integration and continuous delivery (CI/CD), which combines the SDLC into one integrated workflow.

DevOps follows the lean and agile SDLC methodologies and emphasizes collaboration. Throughout the entire SDLC, developers, IT operations staff, and other software teams such as product teams regularly communicate and work together to ensure successful project delivery. Verifying security is another critical part of this process. Developer security operations (DevSecOps) is an evolution of DevOps that helps ensure code security throughout the development process and brings security teams into the fold.

See a comparison of DevOps solutions

Conclusion

A well-structured SDLC helps development teams deliver high-quality software faster and more efficiently. Although SDLC methods vary by organization, most development teams use SDLC to guide their projects. And with AI increasingly integrated throughout the development process, teams can achieve even greater efficiency, quality, and innovation in their software delivery.

The SDLC helps development teams build software that meets user requirements and is well-tested, highly secure, and production ready. Popular tools that support the SDLC process include:

• GitHub Actions to automate SDLC workflows.

• GitHub security tools to help developers ship secure applications.

• GitHub Copilot to developers write code faster with AI.

• GitHub code review tools to help avoid human error.