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Duration 14 hours
Course Outline
AI in the Requirements and Planning Phase
- Applying NLP and LLMs for detailed requirement analysis.
- Translating stakeholder input into epics and user stories.
- Employing AI tools for story refinement and generating acceptance criteria.
AI-Augmented Design and Architecture
- Utilizing AI to model system components and map dependencies.
- Generating architecture diagrams and suggesting UML structures.
- Validating designs through prompt-based system reasoning.
AI-Enhanced Development Workflows
- Assisting code generation and boilerplate scaffolding with AI.
- Refactoring code and improving performance using LLMs.
- Integrating AI tools into IDEs (e.g., Copilot, Tabnine, CodeWhisperer).
Testing with AI
- Creating unit and integration tests using AI models.
- Supporting regression analysis and test maintenance with AI assistance.
- Generating exploratory and boundary test cases with AI.
Documentation, Review, and Knowledge Sharing
- Automatically generating documentation from code and APIs.
- Automating code reviews using AI prompts and structured 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 enhancements.
- Providing intelligent suggestions for canary releases and rollbacks.
- Applying AI to deployment verification and post-deploy analysis.
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI usage and mitigating bias in generated code.
- Maintaining auditing and compliance within AI-assisted workflows.
- Developing a roadmap for the phased adoption of AI across the SDLC.
Summary and Next Steps
Requirements
- A solid understanding 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 (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
