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 Duration 14 hours

Course Outline

Application of AI in the Requirements and Planning Phase

  • Utilizing Natural Language Processing (NLP) and Large Language Models (LLMs) for requirements analysis
  • Translating stakeholder input into epics and user stories
  • Employing AI tools for refining stories and generating acceptance criteria

AI-Augmented Design and Architecture

  • Leveraging AI to model system components and dependencies
  • Generating architectural diagrams and UML suggestions
  • Validating designs through prompt-based system reasoning

AI-Enhanced Development Workflows

  • AI-assisted code generation and boilerplate scaffolding
  • Refactoring code and enhancing performance 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
  • Generating exploratory and boundary cases with AI

Documentation, Review, and Knowledge Sharing

  • Automatic documentation generation from code and APIs
  • Automating code review 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 recommendations
  • Applying AI to deployment verification and post-deploy analysis

Governance, Ethics, and Implementation Strategy

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

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