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 Duration 14 hours

Course Outline

AI in the Requirements and Planning Phase

  • Applying NLP and LLMs for requirement analysis.
  • Translating stakeholder inputs into epics and user stories.
  • Leveraging AI tools for story refinement and generating acceptance criteria.

AI-Augmented Design and Architecture

  • Utilizing AI to model system components and dependencies.
  • Creating architecture diagrams and suggesting UML structures.
  • Validating designs through prompt-based system reasoning.

AI-Enhanced Development Workflows

  • Assisting code generation and boilerplate scaffolding with AI.
  • Refactoring code and boosting 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.
  • Exploratory and boundary case generation with AI.

Documentation, Review, and Knowledge Sharing

  • Automatic documentation generation from code and APIs.
  • Automating code reviews using AI prompts and checklists.
  • Building 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 suggestions.
  • AI in deployment verification and post-deploy analysis.

Governance, Ethics, and Implementation Strategy

  • Ensuring responsible AI use and avoiding 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

  • A solid grasp of software development lifecycle concepts.
  • Practical experience in software architecture or team leadership.
  • Familiarity with DevOps, agile methodologies, or SDLC tooling.

Intended Audience

  • Software architects.
  • Development leads.
  • Engineering managers.

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