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 Duration 14 hours

Course Outline

AI in the Requirements and Planning Stage

  • Leveraging NLP and LLMs for requirement analysis
  • Transforming stakeholder feedback into epics and user stories
  • Employing AI tools to refine stories and generate acceptance criteria

AI-Boosted Design and Architecture

  • Utilizing AI to map system components and dependencies
  • Creating architecture diagrams and UML recommendations
  • Validating designs through prompt-based system reasoning

AI-Optimized Development Workflows

  • AI-supported code generation and boilerplate setup
  • Refactoring code and enhancing performance via LLMs
  • Embedding AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)

AI in Testing

  • Creating unit and integration tests using AI models
  • AI-supported regression analysis and test upkeep
  • Generating exploratory and boundary cases with AI

Documentation, Review, and Knowledge Dissemination

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

AI in CI/CD and Deployment Automation

  • Optimizing pipelines and conducting risk-based testing with AI
  • Providing intelligent canary release and rollback recommendations
  • Applying AI to deployment verification and post-deployment analysis

Governance, Ethics, and Implementation Strategy

  • Ensuring responsible AI usage and preventing bias in generated code
  • Conducting audits and ensuring compliance in AI-assisted workflows
  • Developing a roadmap for phased AI integration across the SDLC

Conclusion and Future Steps

Requirements

  • A solid grasp of software development lifecycle fundamentals
  • Practical experience in software architecture or leading development teams
  • Knowledge of DevOps, agile methodologies, or SDLC-related tooling

Target Audience

  • Software architects
  • Development leads
  • Engineering managers

Testimonials (1)

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