Code2Que: a tool for improving question titles from mined code snippets in stack overflow

Zhipeng Gao, Xin Xia, David Lo, John Grundy, Yuan-Fang Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Stack Overflow is one of the most popular technical Q&A sites used by software developers. Seeking help from Stack Overflow has become an essential part of software developers' daily work for solving programming-related questions. Although the Stack Overflow community has provided quality assurance guidelines to help users write better questions, we observed that a significant number of questions submitted to Stack Overflow are of low quality. In this paper, we introduce a new web-based tool, Code2Que, which can help developers in writing higher quality questions for a given code snippet. Code2Que consists of two main stages: offline learning and online recommendation. In the offline learning phase, we first collect a set of good quality code snippet, question»pairs as training samples. We then train our model on these training samples via a deep sequence-to-sequence approach, enhanced with an attention mechanism, a copy mechanism and a coverage mechanism. In the online recommendation phase, for a given code snippet, we use the offline trained model to generate question titles to assist less experienced developers in writing questions more effectively. To evaluate Code2Que, we first sampled 50 low quality code snippet, question»pairs from the Python and Java datasets on Stack Overflow. Then we conducted a user study to evaluate the question titles generated by our approach as compared to human-written ones using three metrics: Clearness, Fitness and Willingness to Respond. Our experimental results show that for a large number of low-quality questions in Stack Overflow, Code2Que can improve the question titles in terms of Clearness, Fitness and Willingness measures.

Original languageEnglish
Title of host publicationProceedings of the 29th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
EditorsDiomidis Spinellis, Marsha Chechik
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1525-1529
Number of pages5
ISBN (Electronic)9781450385626
DOIs
Publication statusPublished - 20 Aug 2021
EventJoint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2021 - Athens, Greece
Duration: 23 Aug 202128 Aug 2021
Conference number: 29th
https://dl.acm.org/doi/proceedings/10.1145/3468264 (Proceedings)
https://2021.esec-fse.org (Website)

Conference

ConferenceJoint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2021
Abbreviated titleESEC/FSE 2021
Country/TerritoryGreece
CityAthens
Period23/08/2128/08/21
Internet address

Keywords

  • Deep Learning
  • Question Quality
  • Seq2Seq Model
  • Stack Overflow

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