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Course Outline
Fundamentals of Generative AI in Software Engineering
- Core concepts of Generative AI
- The impact of AI on the SDLC
- Survey of AI-powered development tools
AI-Enhanced Coding
- Predictive coding capabilities
- Leveraging code generation and autocomplete features
- Improving code quality through AI-driven insights
Intelligent Debugging
- Automation of error detection
- AI applications in static code analysis
- Dynamic analysis supported by AI
AI in Code Review
- Streamlining code review workflows
- AI-generated suggestions for code optimization
- Maintaining coding standards with AI assistance
Root Cause Analysis via AI
- Data-centric problem-solving strategies
- AI algorithms for pinpointing issues
- Using predictive analytics to avert future errors
Case Studies
- Practical examples of AI integration in the SDLC
- Success narratives and key takeaways
- Emerging trends in AI for software development
Practical Workshops
- Interactive sessions utilizing AI coding tools
- Collaborative projects focusing on AI-assisted debugging
- Peer reviews based on AI-generated insights
Ethics and Best Practices
- Responsible use of AI in software engineering
- Best practices for AI integration into the SDLC
- Striking a balance between human expertise and AI capabilities
Recap and Future Directions
Requirements
- A solid foundation in core software development principles
- Practical experience with at least one programming language
- Proficiency with standard software development tools and environments
Target Audience
- Software Developers
- Technical Team Leads
- Product Managers
21 Hours
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.