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Duration 21 hours
Course Outline
Foundational Concepts of Generative AI in Software Development
- Conceptual understanding of Generative AI
- The function of AI within the SDLC
- Survey of AI-powered development tools
AI-Supported Coding Practices
- Predictive coding enabled by AI
- Utilizing code generation and autocompletion utilities
- Improving code quality through AI-derived insights
Intelligent Debugging Strategies
- Automated identification of errors
- The role of AI in static code analysis
- Conducting dynamic analysis with AI assistance
AI in the Code Review Process
- Streamlining code review procedures
- Leveraging AI for code optimization recommendations
- Maintaining code standards through AI monitoring
AI-Driven Root Cause Analysis
- Adopting a data-centric approach to problem resolution
- Using AI algorithms to pinpoint issues
- Applying predictive analytics to prevent future errors
Real-World Case Studies
- Practical examples of AI integration in the SDLC
- Analysis of success stories and key takeaways
- Emerging trends in AI for software engineering
Practical Workshop Sessions
- Interactive exercises utilizing AI coding tools
- Collaborative projects focused on AI-assisted debugging
- Peer reviews informed by AI-generated observations
Ethics and Professional Best Practices
- Ethical deployment of AI in software development
- Best practices for embedding AI into the SDLC
- Harmonizing human expertise with AI capabilities
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental software development principles
- Practical experience with at least one programming language
- Proficiency with common software development tools and environments
Target Audience
- Software developers
- Technical team leads
- Product managers
Testimonials (1)
I like hands-on experience and engaging audience with content. I also really like that trainer is such a passionate person about technology. He had good energy.