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Duration 14 hours
Course Outline
AI in the Requirements and Planning Phase
- Leveraging NLP and LLMs for requirement analysis
- Translating stakeholder input into epics and user stories
- Applying AI tools for story refinement and generating acceptance criteria
AI-Augmented Design and Architecture
- Utilizing AI to model system components and dependencies
- Generating architecture diagrams and UML suggestions
- Validating designs through prompt-based system reasoning
AI-Enhanced Development Workflows
- AI-assisted code generation and boilerplate scaffolding
- Refactoring code and improving performance with LLMs
- Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)
Testing with AI
- Creating unit and integration tests using AI models
- AI-assisted regression analysis and test maintenance
- Generating exploratory and boundary cases with AI
Documentation, Review, and Knowledge Sharing
- Automating documentation generation 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
- Optimizing pipelines and conducting risk-based testing with AI
- Providing intelligent canary release and rollback recommendations
- Utilizing AI for deployment verification and post-deploy analysis
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI usage and mitigating bias in generated code
- Managing audits 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 principles
- Background in software architecture or team leadership
- Proficiency with DevOps, agile methodologies, or SDLC tools
Target Audience
- Software architects
- Development leads
- Engineering managers
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
