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 Duration 14 hours

Course Outline

Applying AI in the Requirements and Planning Phase

  • Leveraging NLP and LLMs for requirements analysis
  • Translating stakeholder input into epics and user stories
  • Employing AI tools for story refinement and generating acceptance criteria

AI-Supported Design and Architecture

  • Using AI to map system components and their dependencies
  • Creating architecture diagrams and UML recommendations
  • Validating designs via prompt-based system reasoning

Optimized Development Workflows with AI

  • AI-assisted code creation and boilerplate structuring
  • Refactoring code and boosting performance with LLMs
  • Embedding AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)

AI-Powered Testing

  • Producing unit and integration tests with AI models
  • AI-supported regression analysis and test upkeep
  • Generating exploratory and boundary cases using 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
  • Providing intelligent suggestions for canary releases and rollbacks
  • Utilizing AI for deployment verification and post-deploy analysis

Governance, Ethics, and Implementation Strategy

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

Recap and Future Actions

Requirements

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

Target Audience

  • Software architects
  • Development leads
  • Engineering managers

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