Reimagine Software Development Life Cycle (SDLC) with AI

Discover how the software development life cycle transforms business requirements into software. Follow the phases—planning, design, implementation, testing, deployment, and maintenance—and note loops and models like waterfall, agile, and scrum.

Agentic AI coordinates multiple specialized agents to autonomously plan and execute multi-step goals, transforming natural language tasks into comprehensive reports with charts and insights.

Reimagine the SDLC with agentic AI that can plan, reason, and act alongside humans, writing specs, designing APIs, coding, and testing while humans retain oversight and accountability.

Explore how GitHub Copilot evolves from coding assistant to autonomous ai agent that supports the software development life cycle tasks, including code completion, bug fixes, documentation, and testing.

Register a GitHub account, activate GitHub Copilot, and install Visual Studio Code with Copilot integration to access premium features for coding.

Master clear, specific prompts to guide AI tools like GitHub Copilot, turning instructions into precise, useful actions and enhancing collaboration across the software development life cycle.

Explore AI-assisted requirement analysis that gathers stakeholder-driven functional and non-functional requirements, documents them for sign-off, and guides a car rental MVP with GitHub Copilot space.

Get quick Git concepts for GitHub: repositories, branches, the main branch, and pull requests as safe checkpoints to review, discuss, and merge changes.

Organize work with GitHub projects using a kanban board to track backlog, ready, and in-progress tasks, forming a single source of truth for the SDLC.

create a PRD for each business role by turning interview results into small, detailed issues in GitHub, then use Copilot to generate markdown PRDs and review them against organizational standards.

Establish and enforce artifact standards across the SDLC while leveraging GitHub Copilot with clearly defined constraints and human verification.

Examine the cost and quota constraints of GitHub Copilot, including premium requests and action minutes, and identify which tasks benefit from Copilot versus those needing human effort.

Learn how to manage PRD workflows with Copilot by closing and regenerating PRDs, reassigning issues, and moving items to in-progress and in-review. Humans review and merge PRDs to ensure standards.

Explore validating AI-generated content within a coordinated system, and how Copilot acts as a smart team member whose tasks require explicit context across PRDs and pull requests.

Configure a default pull request commit template in GitHub to produce clear human friendly messages. Learn how to describe changes made by Copilot and improve traceability in your git history.

Translate a product requirement document into a focused technical requirement document using AI, breaking PRD into feature-level TRDs with architecture, interfaces, data models, and non-functional needs.

Reimagine the sdlc with ai by examining copilot-driven trd table consolidation across pull requests. Balance automation with human judgment to manage context and costs.

Explore context-aware tasks in the software development life cycle by using the vehicles table as a reference, applying modifications across prd and trd, and guiding GitHub Copilot workflows.

UI/UX design takes center stage in the system design phase, creating a user interface mockup, screen templates, and navigation paths with tools like Figma, Sketch, or Adobe XD.

Translate requirements into working software in the implementation phase by coding, unit testing, and using GitHub Copilot within Visual Studio Code to deploy Go and JavaScript features to development.

Separate the source code repository from the documentation repository to enable targeted access and faster backend development with Go and GitHub Copilot.

Prepare your development environment by installing Visual Studio Code, the Go and Markdown extensions, and GitHub Copilot, then clone the source repository and open it in VS Code.

Review pull requests generated by Copilot, assess AI-created code and documentation, update database configuration and SQL migration, and iteratively fix errors via Copilot chat until ready to merge.

Define and share a coding standard to align teams and accelerate onboarding, using a Go baseline with a REST API and PostgreSQL, guided by documented guidelines for GitHub Copilot.

Implement a database structure with SQL and Go code, guided by the technical requirement document. Copilot autonomously creates code and pull requests; review and merge to main.

Learn API implementation workflows by creating one issue per API functionality, leveraging Copilot to generate models, handlers, tests, and PRs, and resolving merge conflicts with human judgment.

Use GitHub Copilot in VS Code by attaching local file context or the whole codebase when none is open, and control context with slash, explain, hash, or url references.

Extend GitHub Copilot with an MCP server to connect to external tools via Model Context Protocol, enabling task delegation, issue and pull request access, and coordinated local workflows.

Compare the GitHub Copilot MCP server in Visual Studio Code versus the web interface, noting premium request costs, issue-to-pull-request workflows, and cross-server orchestration.

Initialize a Solid JS frontend repository with a coding standard and GitHub setup, then leverage Figma designs and MCP-assisted Copilot to build and refine the car list page.

Learn how GitHub Copilot and MCP integrate testing on the web, configure Figma MCP servers, and manage car detail tasks via issues and pull requests.

Configure Copilot's firewall to restrict internet access and block unsafe requests, with warnings in pull requests; customize the allowlist in repository settings under Copilot, Coding Agent.

Verify functional and non-functional requirements through testing, identify bugs, and validate features using test scenarios and test cases, including positive and negative and performance testing with Copilot and K6.

Create and organize test scenarios in the docs/test-scenario subfolder, track issues on project board, review Copilot-generated test cases for prds like car management and marketing, and merge the pull request.

Create performance tests for REST APIs using GUI-based tools (Postman, JMeter, Cypress) and script-based tools (K6, Artillery), generating scripts with Copilot from Postman collections and validating API metrics.

Deploy microservice applications with containerization and Kubernetes in cloud environments using infrastructure-as-code practices. Leverage GitHub Copilot to generate deployment scripts, manifests, and Helm charts for repeatable, automated deployment.

Learn to containerize a Go application with docker by creating a dockerfile and image, run as a container. Configure environment variables at runtime and validate via health checks and endpoints.

Demonstrates using GitHub Copilot to generate a Kubernetes deployment script and outlines real-world complexities, DevOps flow, and testing, branching, and merging it into main.

Maintain software in production, fix bugs, enhance performance, patch security vulnerabilities, and introduce features that may trigger new SDLC cycles; Copilot aids coding tasks while monitoring remains separate.