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AI Research
arXiv CS.AI·3d ago·5 min read
Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development
TL;DR
ReqClarifyBench, a new benchmark for evaluating interactive requirement clarification in software development, reveals that better clarification does not always lead to higher quality requirement documents. Performance varies significantly across different user personas.
✦ Why It Matters
Evaluate your LLM's requirement clarification capabilities to enhance software development processes today.
Key Takeaways
- Assess LLMs for their ability to clarify vague requirements.
- Utilize ReqClarifyBench to benchmark your models effectively.
- Consider user persona diversity when evaluating LLM performance.
- Recognize that better clarification does not always mean better documentation.
Full Summary
As large language models (LLMs) advance in software implementation, understanding user intent becomes crucial, especially when initial requests are vague. Existing benchmarks often overlook the importance of requirement clarification before coding, focusing instead on complete specifications.
To address this gap, ReqClarifyBench was developed, featuring 341 tasks across various domains like game and web development. Each task includes a vague user intent, a validated requirement document, and code evaluation artifacts.
The study evaluated six LLMs using user simulators with diverse personas to assess clarification effectiveness and document quality. Results indicated that improved clarification effectiveness does not guarantee better document quality, and performance varied widely among user personas.
Additionally, using coding-agent harnesses did not enhance document quality compared to base LLMs, providing insights for future LLM-based software development systems.