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Duration 14 hours
Course Outline
AI in Requirements and Planning
- Leveraging NLP and LLMs to analyze and interpret requirements.
- Translating stakeholder feedback into detailed epics and user stories.
- Employing AI tools to refine stories and automatically generate acceptance criteria.
AI-Enhanced Design and Architecture
- Modeling system components and dependencies with the aid of AI.
- Generating architecture diagrams and UML diagrams based on AI suggestions.
- Validating designs through prompt-based system reasoning techniques.
AI-Optimized Development Workflows
- Accelerating development with AI-assisted code generation and scaffolding.
- Refactoring code and boosting performance using LLM capabilities.
- Integrating AI assistants like Copilot, Tabnine, or CodeWhisperer directly into IDEs.
AI in Testing
- Creating unit and integration tests using advanced AI models.
- Maintaining tests and performing regression analysis with AI assistance.
- Discovering exploratory and boundary cases through AI generation.
Documentation, Review, and Knowledge Management
- Automatically generating documentation from codebases and APIs.
- Automating code reviews utilizing AI prompts and standardized checklists.
- Building interactive knowledge bases and FAQs using conversational AI.
AI in CI/CD and Deployment Automation
- Optimizing pipelines and applying risk-based testing strategies with AI.
- Receiving intelligent recommendations for canary releases and rollbacks.
- Utilizing AI for deployment verification and post-release analysis.
Governance, Ethics, and Implementation Strategy
- Ensuring responsible AI usage and mitigating bias in generated code.
- Maintaining auditing standards and compliance within AI-assisted workflows.
- Developing a strategic roadmap for phased AI adoption across the SDLC.
Conclusion and Future Directions
Requirements
- A solid grasp of software development lifecycle fundamentals.
- Background experience in software architecture or team leadership roles.
- Working knowledge of DevOps, agile methodologies, or SDLC-related tools.
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
