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Course Outline
AI in the Requirements and Planning Phase
- Using NLP and LLMs for requirement analysis
- Converting stakeholder input into epics and user stories
- AI tools for story refinement and acceptance criteria generation
AI-Augmented Design and Architecture
- Using AI to model system components and dependencies
- Generating architecture diagrams and UML suggestions
- Design validation through prompt-based system reasoning
AI-Enhanced Development Workflows
- AI-assisted code generation and boilerplate scaffolding
- Code refactoring and performance improvements using LLMs
- Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)
Testing with AI
- Generating unit and integration tests using AI models
- AI-assisted regression analysis and test maintenance
- Exploratory and boundary case generation with AI
Documentation, Review, and Knowledge Sharing
- Automatic documentation generation from code and APIs
- Code review automation using AI prompts and checklists
- Creating knowledge bases and FAQs using conversational AI
AI in CI/CD and Deployment Automation
- AI-enhanced pipeline optimization and risk-based testing
- Intelligent canary release and rollback suggestions
- AI in deployment verification and post-deploy analysis
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI use and avoiding bias in generated code
- Auditing and compliance in AI-assisted workflows
- Building a roadmap for phased AI adoption across SDLC
Summary and Next Steps
Requirements
- An understanding of software development lifecycle concepts
- Experience in software architecture or team leadership
- Familiarity with DevOps, agile practices, or SDLC tooling
Audience
- Software architects
- Development leads
- Engineering managers
14 Hours
