Devnexus 2025 - TDD & Generative AI A Perfect Pairing - Bouke Nijhuis
AI Summary
Summary of “TDD and Gen: A Perfect Pairing”
- Introduction
- Talk on Test Driven Development (TDD) and Generative AI (Gen AI).
- Previous discussions focused on AI coding assistants’ capability in generating test cases.
- Key Concepts
- Generative AI: AI coding assistants like GitHub Copilot, JetBrains AI, and GPT.
- Test Driven Development (TDD): Writing tests in advance leads to higher-quality implementations.
- Pair Programming: Two-person approach to coding, enhancing collaboration.
- Proposed Approach: Combining TDD, Gen AI, and Pair Programming.
- Test Driven Generation (TDG): Human writes tests; AI generates corresponding implementations.
- Tests are used both as inputs for AI and for validating implementations.
- Benefits of Using AI for Code Generation
- AI can produce initial implementations.
- Automated testing ensures correctness of AI-generated code.
- Illustrative Example:
- Demonstration involved creating a simple “Odd/Even” test and generating its implementation using AI.
- Challenges in manual steps were discussed, emphasizing automation of this process.
- Local vs. Cloud Models: Discussed different environments for AI models.
- Local models are faster but might be less powerful than cloud models.
- Future Improvements:
- Expanding capabilities to handle various languages and frameworks.
- Developing an IDE plugin to streamline the coding process.
- Conclusion:
- TDD and Generative AI form a synergistic relationship.
- The model proposed effectively leads to production-ready code, with automated validation possible.