Automatic prediction of rejected edits in Stack Overflow
The content quality of shared knowledge in Stack Overflow (SO) is crucial in supporting software developers with their programming problems. Thus, SO allows its users to suggest edits to improve the quality of a post (i.e., question and answer). However, existing research shows that many suggested edits in SO are rejected due to undesired contents/formats or violating edit guidelines. Such a scenario frustrates or demotivates users who would like to conduct good-quality edits. Therefore, our research focuses on assisting SO users by offering them suggestions on how to improve their editing of posts. First, we manually investigate 764 (382 questions + 382 answers) rejected edits by rollbacks and produce a catalog of 19 rejection reasons. Second, we extract 15 texts and user-based features to capture those rejection reasons. Third, we develop four machine learning models using those features. Our best-performing model can predict rejected edits with 69.1% precision, 71.2% recall, 70.1% F1-score, and 69.8% overall accuracy. Fourth, we introduce an online tool named EditEx that works with the SO edit system. EditEx can assist users while editing posts by suggesting the potential causes of rejections. We recruit 20 participants to assess the effectiveness of EditEx. Half of the participants (i.e., treatment group) use EditEx and another half (i.e., control group) use the SO standard edit system to edit posts. According to our experiment, EditEx can support SO standard edit system to prevent 49% of rejected edits, including the commonly rejected ones. However, it can prevent 12% rejections even in free-form regular edits. The treatment group finds the potential rejection reasons identified by EditEx influential. Furthermore, the median workload suggesting edits using EditEx is half compared to the SO edit system.
Fri 19 MayDisplayed time zone: Hobart change
11:00 - 12:30 | Developers' forumsSEIP - Software Engineering in Practice / Journal-First Papers / Technical Track / DEMO - Demonstrations at Meeting Room 102 Chair(s): Omar Haggag Monash University, Australia | ||
11:00 15mTalk | Automatic prediction of rejected edits in Stack Overflow Journal-First Papers Saikat Mondal University of Saskatchewan, Gias Uddin University of Calgary, Canada, Chanchal K. Roy University of Saskatchewan Link to publication DOI Pre-print | ||
11:15 15mTalk | Automated Summarization of Stack Overflow Posts Technical Track Bonan Kou Purdue University, Muhao Chen University of Southern California, Tianyi Zhang Purdue University | ||
11:30 15mTalk | Semi-Automatic, Inline and Collaborative Web Page Code Curations Technical Track Roy Rutishauser University of Zurich, André N. Meyer University of Zurich, Reid Holmes University of British Columbia, Thomas Fritz University of Zurich | ||
11:45 15mTalk | You Don’t Know Search: Helping Users Find Code by Automatically Evaluating Alternative Queries SEIP - Software Engineering in Practice Rijnard van Tonder Sourcegraph | ||
12:00 7mTalk | TECHSUMBOT: A Stack Overflow Answer Summarization Tool for Technical Query DEMO - Demonstrations Chengran Yang Singapore Management University, Bowen Xu Singapore Management University, Jiakun Liu Singapore Management University, David Lo Singapore Management University | ||
12:07 8mTalk | An empirical study of question discussions on Stack Overflow Journal-First Papers Wenhan Zhu University of Waterloo, Haoxiang Zhang Centre for Software Excellence at Huawei Canada, Ahmed E. Hassan Queen’s University, Michael W. Godfrey University of Waterloo, Canada | ||
12:15 15mTalk | Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence Technical Track zejun zhang Australian National University, Zhenchang Xing , Xin Xia Huawei, Xiwei (Sherry) Xu CSIRO’s Data61, Liming Zhu CSIRO’s Data61, Qinghua Lu CSIRO’s Data61 | ||