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Course Outline
AI in the Requirements and Planning Phase
- Employing NLP and LLMs for requirement analysis.
- Converting stakeholder feedback into epics and user stories.
- Utilizing AI tools for story refinement and generating acceptance criteria.
AI-Augmented Design and Architecture
- Using AI to model system components and dependencies.
- Generating architecture diagrams and receiving UML suggestions.
- Validating designs through prompt-based system reasoning.
AI-Enhanced Development Workflows
- Leveraging AI for code generation and creating boilerplate scaffolding.
- Performing code refactoring and enhancing performance using LLMs.
- Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer).
Testing with AI
- Generating unit and integration tests using AI models.
- Utilizing AI for regression analysis and test maintenance assistance.
- Employing AI for exploratory testing and boundary case generation.
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 with conversational AI.
AI in CI/CD and Deployment Automation
- Optimizing pipelines and conducting risk-based testing with AI enhancements.
- Receiving intelligent suggestions for canary releases and rollbacks.
- Applying AI to deployment verification and post-deployment analysis.
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI usage and mitigating bias in generated code.
- Maintaining audit trails and compliance within AI-assisted workflows.
- Developing a roadmap for phased AI adoption across the SDLC.
Summary and Next Steps
Requirements
- A solid understanding of software development lifecycle concepts.
- Experience in software architecture or team leadership.
- Familiarity with DevOps principles, agile practices, or SDLC tooling.
Target Audience
- Software architects.
- Development 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