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Duration 14 hours
Course Outline
AI in the Requirements and Planning Stage
- Applying NLP and LLMs for detailed requirement analysis
- Translating stakeholder feedback into epics and user stories
- Leveraging AI tools for story refinement and generating acceptance criteria
AI-Assisted Design and Architecture
- Modeling system components and dependencies using AI
- Creating architecture diagrams and UML suggestions
AI-Optimized Development Workflows
- Refactoring code and boosting performance using LLMs
- Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)
AI-Driven Testing
- Generating unit and integration tests via AI models
- Assisting with regression analysis and test maintenance using AI
- Creating exploratory and boundary cases with AI
Documentation, Review, and Knowledge Dissemination
- Generating automatic documentation from code and APIs
- Automating code reviews with AI prompts and checklists
- Building knowledge bases and FAQs using conversational AI
AI in CI/CD and Deployment Automation
- Optimizing pipelines and conducting risk-based testing with AI
- Utilizing AI for deployment verification and post-deploy analysis
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI use and mitigating bias in generated code
- Developing a roadmap for phased AI adoption across the SDLC
Wrap-Up and Next Steps
Requirements
- A solid grasp of software development lifecycle fundamentals
- Background experience in software architecture or team leadership
- Working knowledge of 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
