Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks

ArXi:2604.14624v1 Announce Type: cross 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.