TDD Governance for Multi-Agent Code Generation via Prompt Engineering
📰 ArXiv cs.AI
Learn how to apply TDD governance to multi-agent code generation using prompt engineering for more stable and disciplined software development
Action Steps
- Apply TDD principles to multi-agent code generation using prompt engineering
- Configure LLMs to enforce test-driven development constraints
- Run tests to validate AI-generated code
- Refactor code to improve stability and adherence to development discipline
- Compare results with traditional TDD approaches to evaluate effectiveness
Who Needs to Know This
Software engineers and AI researchers can benefit from this approach to improve the quality and reliability of AI-generated code, especially in multi-agent systems
Key Insight
💡 TDD governance can be applied to multi-agent code generation using prompt engineering to ensure more stable and disciplined software development
Share This
🚀 Improve AI-generated code quality with TDD governance for multi-agent code generation via prompt engineering! 💻
Key Takeaways
Learn how to apply TDD governance to multi-agent code generation using prompt engineering for more stable and disciplined software development
Full Article
Title: TDD Governance for Multi-Agent Code Generation via Prompt Engineering
Abstract:
arXiv:2604.26615v1 Announce Type: cross Abstract: Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM-based approaches typically use tests as auxiliary inputs rather than enforceable process constraints. We present an AI-native TDD framework that operationalizes classica
Abstract:
arXiv:2604.26615v1 Announce Type: cross Abstract: Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) provides a structured Red-Green-Refactor process, existing LLM-based approaches typically use tests as auxiliary inputs rather than enforceable process constraints. We present an AI-native TDD framework that operationalizes classica
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