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Duration 14 hours
Course Outline
Applying AI to the Requirements and Planning Phase
- Leveraging NLP and LLMs for detailed requirement analysis
- Transforming stakeholder feedback into epics and user stories
- Utilizing AI tools for story refinement and creating acceptance criteria
AI-Augmented Design and Architecture
- Modeling system components and dependencies with the help of AI
- Generating architecture diagrams and UML suggestions
- Validating designs through prompt-based system reasoning
AI-Optimized Development Workflows
- AI-assisted code generation and boilerplate scaffolding
- Refactoring code and improving performance using LLMs
- Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)
AI-Powered Testing
- Generating unit and integration tests via AI models
- AI-assisted regression analysis and test maintenance
- Generating exploratory and boundary cases with AI
Documentation, Review, and Knowledge Management
- Automating documentation generation from code and APIs
- Automating code reviews using AI prompts and checklists
- Developing knowledge bases and FAQs using conversational AI
AI in CI/CD and Deployment Automation
- Optimizing pipelines and implementing 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 within AI-assisted workflows
- Developing a roadmap for phased AI adoption across the SDLC
Conclusion and Future Directions
Requirements
- A solid grasp of software development lifecycle principles
- Professional experience in software architecture or team leadership
- Proficiency with DevOps, agile methodologies, or SDLC-related 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
