Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2021

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.

Keywords

Stack Overflow, Question Quality, Seq2Seq Model, Deep Learning

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ESEC/FSE '21: Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Online, August 23-28

First Page

1525

Last Page

1529

ISBN

9781450385626

Identifier

10.1145/3468264.3473114

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3468264.3473114

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