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 Duration 14 hours

Course Outline

Integrating AI into Requirements and Planning

  • Applying NLP and LLMs to analyze requirements
  • Translating stakeholder feedback into epics and user stories
  • Employing AI to refine stories and define acceptance criteria

AI-Supported Design and Architecture

  • Leveraging AI to map system components and dependencies
  • Creating architecture diagrams and generating UML suggestions
  • Validating designs through prompt-based system reasoning

Optimizing Development Workflows with AI

  • AI-assisted code generation and creating boilerplate structures
  • Enhancing code structure and performance via LLMs
  • Incorporating AI assistants into IDEs (such as Copilot, Tabnine, CodeWhisperer)

Intelligent Testing Strategies

  • Creating unit and integration tests with AI models
  • Using AI to aid regression testing and maintenance
  • Generating exploratory and boundary cases using AI

Documentation, Review, and Knowledge Management

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

AI in CI/CD and Deployment Automation

  • Optimizing pipelines and implementing risk-based testing with AI
  • Suggesting intelligent canary releases and rollbacks
  • Utilizing AI for deployment verification and post-release analysis

Governance, Ethics, and Adoption Strategy

  • Promoting responsible AI use and mitigating bias in code generation
  • Ensuring auditability and compliance in AI-assisted processes
  • Developing a roadmap for phased AI integration across the SDLC

Recap and Future Directions

Requirements

  • Solid grasp of software development lifecycle fundamentals
  • Background in software architecture or leading development teams
  • Working knowledge of DevOps, agile methodologies, or SDLC tools

Target Audience

  • Software architects
  • Development leads
  • Engineering managers

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