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Course Outline

AI in the Requirements and Planning Phase

  • Leveraging NLP and LLMs for requirement analysis
  • Translating stakeholder input into epics and user stories
  • Using AI tools to refine stories and generate acceptance criteria

AI-Augmented Design and Architecture

  • Using AI to model system components and dependencies
  • Generating architecture diagrams and receiving UML suggestions
  • Validating designs through prompt-based system reasoning

AI-Enhanced Development Workflows

  • AI-assisted code generation and creating boilerplate scaffolding
  • Refactoring code and improving performance with LLMs
  • Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)

Testing with AI

  • Generating unit and integration tests using AI models
  • Conducting regression analysis and test maintenance with AI assistance
  • Generating exploratory and boundary cases with AI

Documentation, Review, and Knowledge Sharing

  • Automatically generating documentation from code and APIs
  • Automating code reviews using AI prompts and checklists
  • Building knowledge bases and FAQs with conversational AI

AI in CI/CD and Deployment Automation

  • Optimizing pipelines and conducting risk-based testing with AI
  • Receiving intelligent suggestions for canary releases and rollbacks
  • Utilizing AI for deployment verification and post-deployment analysis

Governance, Ethics, and Implementation Strategy

  • Ensuring responsible AI usage and mitigating bias in generated code
  • Auditing and maintaining compliance within AI-assisted workflows
  • Developing a roadmap for phased AI adoption across the SDLC

Summary and Next Steps

Requirements

  • A foundational understanding of software development lifecycle concepts
  • Experience in software architecture or team leadership
  • Familiarity with DevOps methodologies, agile practices, or SDLC tooling

Target Audience

  • Software architects
  • Development leads
  • Engineering managers
 14 Hours

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