GepAI in Development Processes and Dev Tools
summary
Claims that large language models identify gaps in requirements and turn user feedback into ready tickets, demonstrate concrete use of GitHub copilot and Cursor and chpt for prototyping in Python and JavaScript, and report Meta work on automated unit test improvement that generates unit and end to end tests. Shows program repair, automated logging, codebase search, and PR review helpers working in limited scopes. Notes increased code churn, poorer maintainability on generated code, and failures on rare languages like Rust.
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chapters (10)
- 0:00Introduction to Generative AI in Development
- 0:40Overview of AI in Software Development
- 2:27Understanding SDLC and Current Trends
- 4:11The Future of Automated SDLC
- 5:46LLMs in Requirements Engineering
- 7:44LLMs in Software Development
- 10:17LLMs in Software Maintenance
- 14:21LLMs in Testing and Documentation
- 19:34Challenges and Side Effects of LLMs
- 21:28Conclusion and Final Thoughts
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