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 Duration 14 hours

Course Outline

AI in the Requirements and Planning Stage

  • Applying NLP and LLMs for requirements analysis
  • Turning stakeholder feedback into epics and user stories
  • Utilizing AI utilities for refining stories and generating acceptance criteria

AI-Enhanced Design and Architecture

  • Employing AI to model system components and their dependencies
  • Creating architecture diagrams and UML recommendations
  • Validating designs through prompt-based system reasoning

AI-Boosted Development Workflows

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

Testing Leveraging AI

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

Documentation, Review, and Knowledge Transfer

  • Auto-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

  • Enhancing pipeline optimization and risk-based testing with AI
  • Suggesting intelligent canary releases and rollbacks
  • Applying AI to deployment verification and post-deployment analysis

Governance, Ethics, and Implementation Strategy

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

Recap and Future Steps

Requirements

  • Familiarity with the fundamentals of the software development life cycle
  • Practical experience in software architecture or leading development teams
  • Knowledge of DevOps, agile methodologies, or SDLC-related tools

Target Audience

  • Software architects
  • Development leads
  • Engineering managers

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