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Duration 14 hours
Course Outline
AI Integration in Requirements and Planning
- Applying NLP and LLMs for in-depth requirement analysis
- Transforming stakeholder feedback into epics and user stories
- Employing AI tools for refining stories and generating acceptance criteria
AI-Enhanced Design and Architecture
- Leveraging AI to map system components and identify dependencies
- Creating architecture diagrams and proposing UML structures
- Validating designs through prompt-based system reasoning
AI-Boosted Development Workflows
- Assisting code generation and setting up boilerplate scaffolds with AI
- Refactoring code and optimizing performance using LLMs
- Embedding AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer)
AI in Testing
- Producing unit and integration tests via AI models
- Using AI to support regression analysis and maintain test suites
- Generating exploratory and boundary test cases with AI assistance
Documentation, Code Review, and Knowledge Transfer
- Auto-generating documentation from codebases and APIs
- Automating code reviews with AI prompts and standardized checklists
- Building 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 recommendations for canary releases and rollbacks
- Applying AI for deployment verification and post-release analysis
Governance, Ethics, and Implementation Strategy
- Promoting responsible AI usage and mitigating bias in generated code
- Ensuring auditing and compliance within AI-assisted workflows
- Developing a roadmap for the phased adoption of AI across the SDLC
Summary and Recommended Next Steps
Requirements
- A solid grasp of software development lifecycle fundamentals
- Experience in software architecture or leading development teams
- Proficiency with DevOps, agile methodologies, or SDLC-related 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
