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Duration 14 hours
Course Outline
AI in Requirements and Planning
- Leveraging NLP and LLMs for requirement analysis
- Translating stakeholder input into epics and user stories
- Utilizing AI tools for story refinement and acceptance criteria creation
AI-Augmented Design and Architecture
- Modeling system components and dependencies with AI
- Creating architecture diagrams and UML recommendations
- Validating designs through prompt-based system reasoning
AI-Enhanced 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)
Testing with AI
- Generating unit and integration tests with AI models
- AI-assisted regression analysis and test maintenance
- Exploratory and boundary case generation using AI
Documentation, Review, and Knowledge Sharing
- Auto-generating 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 risk-based testing with AI
- Intelligent canary release and rollback recommendations
- AI in deployment verification and post-deploy analysis
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI use and preventing bias in generated code
- Auditing and compliance within 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
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