Documenting my journey in Software Engineering through the lens of Generative AI: From code comprehension and refactoring to AI-assisted debugging and testing
Learning Generative AI for Software Engineering This repository serves as a professional log of my exploration into Generative AI tools and their application in modern software development. Module Goals • Comprehension: Using AI to break down complex, legacy codebases. • Efficiency: Automating documentation and unit test generation. • Refactoring: Leveraging AI to improve code smell and performance. • Learning: Using LLMs as a "Socratic tutor" for new SDKs and APIs.
My AI Ethics & Principles As an aspiring engineer, I follow these rules when using AI:
- Understand First: Never commit code generated by AI that I cannot explain line-by-line.
- Verify & Test: AI-generated unit tests must be manually verified for logic errors.
- Security First: Never share sensitive API keys or credentials with an LLM.