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Course Outline
AI in the Requirements and Planning Phase
- Employing NLP and LLMs for requirement analysis.
- Transforming stakeholder input into epics and user stories.
- Utilizing AI tools for story refinement and generating acceptance criteria.
AI-Augmented Design and Architecture
- Leveraging AI to model system components and dependencies.
- Generating architecture diagrams and receiving UML suggestions.
- Validating design through prompt-based system reasoning.
AI-Enhanced Development Workflows
- Implementing 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 using AI models.
- Conducting regression analysis and test maintenance with AI assistance.
- Generating exploratory and boundary case tests with AI.
Documentation, Review, and Knowledge Sharing
- Automatically generating documentation from code and APIs.
- Automating code reviews using AI prompts and 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.
- Receiving intelligent suggestions for canary releases and rollbacks.
- Utilizing AI for deployment verification and post-deployment analysis.
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI usage and mitigating bias in generated code.
- Auditing and maintaining compliance in AI-assisted workflows.
- Developing a roadmap for phased AI adoption across the SDLC.
Summary and Next Steps
Requirements
- A foundational understanding of software development lifecycle concepts.
- Experience in software architecture or team leadership roles.
- Familiarity with DevOps principles, agile practices, or SDLC tooling.
Audience
- Software architects.
- Development team leads.
- Engineering managers.
14 Hours
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