TL;DR
This paper presents a conversational technique that guides software developers in manually refining their Web search queries through clarification questions, improving search effectiveness by eliciting additional relevant query terms.
Contribution
It introduces a neural-based recommendation system for clarification questions tailored to software developers' queries, enhancing query reformulation beyond automatic methods.
Findings
The system predicts valid clarification questions 80% of the time.
It outperforms simple and state-of-the-art learning to rank baselines.
Developers find the approach useful across experience levels.
Abstract
Context: Recent research indicates that Web queries written by software developers are not very successful in retrieving relevant results, performing measurably worse compared to general purpose Web queries. Most approaches up to this point have addressed this problem with software engineering-specific automated query reformulation techniques, which work without developer involvement but are limited by the content of the original query. In other words, these techniques automatically improve the existing query but can not contribute new, previously unmentioned, concepts. Objective: In this paper, we propose a technique to guide software developers in manually improving their own Web search queries. We examine a conversational approach that follows unsuccessful queries with a clarification question aimed at eliciting additional query terms, thus providing to the developer a clear…
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