Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2020
Abstract
Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet. The Stack Overflow community recommends users to provide the related code snippet when they are creating a question to help others better understand it and offer their help. Previous studies have shown that a significant number of these questions are of low-quality and not attractive to other potential experts in Stack Overflow. These poorly asked questions are less likely to receive useful answers and hinder the overall knowledge generation and sharing process. Considering one of the reasons for introducing low-quality questions in SO is that many developers may not be able to clarify and summarize the key problems behind their presented code snippets due to their lack of knowledge and terminology related to the problem, and/or their poor writing skills, in this study we propose an approach to assist developers in writing high-quality questions by automatically generating question titles for a code snippet using a deep sequence-to-sequence learning approach. Our approach is fully data-driven and uses an attention mechanism to perform better content selection, a copy mechanism to handle the rare-words problem and a coverage mechanism to eliminate word repetition problem. We evaluate our approach on Stack Overflow datasets over a variety of programming languages (e.g., Python, Java, Javascript, C# and SQL) and our experimental results show that our approach significantly outperforms several state-of-the-art baselines in both automatic and human evaluation. We have released our code and datasets to facilitate other researchers to verify their ideas and inspire the follow up work.
Keywords
Stack Overflow, question generation, question quality, sequence-to-sequence
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ACM Transactions on Software Engineering and Methodology
Volume
29
Issue
4
First Page
1
Last Page
37
ISSN
1049-331X
Identifier
10.1145/3401026
Publisher
ACM
Citation
GAO, Zhipeng; XIA, Xin; GRUNDY, John; LO, David; and LI, Yuan-Fang.
Generating question titles for Stack Overflow from mined code snippets. (2020). ACM Transactions on Software Engineering and Methodology. 29, (4), 1-37.
Available at: https://ink.library.smu.edu.sg/sis_research/5622
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3401026