SDLC : A DevOps Perspective with Azure DevOps

SDLC with Azure DevOps: Driving Value Through Collaboration and Automation

Modern software delivery isn’t just about writing code—it’s about delivering value continuously. The Software Development Life Cycle (SDLC), when powered by Azure DevOps, enables teams to collaborate seamlessly, automate workflows, and deliver reliable applications faster.

Azure DevOps provides end-to-end capabilities across planning, coding, testing, releasing, and operating with tools like Boards, Repos, Pipelines, Test Plans, Artifacts, and Monitor/Insights.

Roles and Responsibilities in SDLC

• Developers: Build features, write unit/integration tests, review pull requests, and resolve quality/security issues flagged by CI checks.

• QA Engineers: Define test strategies, automate regression suites, perform exploratory testing, and enforce quality gates before promotion.

• DevOps Engineers: Design and maintain CI/CD pipelines, environments, and infrastructure as code; apply deployment strategies (blue-green, canary); enable observability and reliability.

• Product Owner: Owns vision and value delivery, manages the backlog in Boards, defines acceptance criteria, and validates outcomes.

• Scrum Master: Acts as a servant-leader, facilitates Agile events, removes impediments, and coaches for flow and continuous improvement.

• Business Analyst (BA): Captures requirements, clarifies acceptance criteria, and maintains shared understanding across stakeholders and teams.

• Architects: Define architecture, non-functional requirements, and integration patterns; evolve systems while capturing architectural decisions alongside code.

• Support/Operations: Monitor system health and SLOs, manage incidents and problems, and feed operational learnings back into the backlog.

SDLC Phases with Azure DevOps

• Requirement & Planning

  • Capture Epics, Features, and Stories in Boards with clear acceptance criteria.

  • Ensure end-to-end traceability by linking work items to code, builds, tests, and releases.

• Analysis & Design

  • Use Wiki and Repos for design documents, diagrams, and Architecture Decision Records (ADRs).

  • Review designs “as code” to keep architecture living and verifiable.

• Coding

  • Develop in Repos (Git) with branch policies, required reviewers, and build validation.

  • CI runs automated checks: compile, unit tests, linting, security scans (SAST), and dependency validation.

• Testing

  • Automate unit, API, integration, and UI tests in Pipelines.

  • Manage exploratory/manual test runs in Test Plans.

  • Apply quality gates and environment checks before promoting releases.

• Deployment

  • Use multi-stage YAML Pipelines with defined environments, approvals, and checks.

  • Promote signed Artifacts across dev → test → staging → production.

  • Employ deployment strategies like canary or blue-green for safer rollouts.

• Operations & Maintenance

  • Instrument applications with Application Insights and Log Analytics.

  • Configure dashboards, alerts, and incident workflows.

  • Link incidents to work items for continuous improvement.

SDLC Models in Practice

• Waterfall – Sequential phases; suited to regulatory or highly stable projects but rigid against change.

• Spiral – Iterative loops with explicit risk analysis; ideal for complex, high-risk domains.

• Prototype / RAD – Rapid prototyping and validation through short cycles; accelerates feedback and reduces misalignment.

• Incremental – Deliver value in slices with continuous testing and customer feedback; works seamlessly with CI/CD.

• Agile – Iterative sprints with continuous planning, reviews, and retrospectives; the most natural fit for DevOps automation and frequent releases.

Conclusion

SDLC provides the framework; DevOps supercharges it. With Azure DevOps, every phase of the lifecycle—from planning to production—is integrated, automated, and measurable. This enables teams not only to build software but to deliver business value continuously and reliably.