Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks
Humans often specify tasks incompletely, so assistants must know when and how to ask clarifying questions. However, effective clarification remains challenging in software engineering tasks as not all missing information is equally valuable, and questions must target information users can realistically provide. We study clarification in real software engineering tasks by quantifying which types of information most affect task success and which questions elicit useful responses from simulated users. Using Shapley attribution and distributional comparisons, we identify two key properties of effective clarification: task relevance (which information predicts success) and user answerability (...
| Authors | Sanidhya Vijayvargiya, Vijay Viswanathan, Graham Neubig |
|---|---|
| Primary category | cs.SE |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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