Automatic Prediction of Rejected Edits in Stack Overflow
Saikat Mondal, Gias Uddin, Chanchal Roy

TL;DR
This paper presents a machine learning-based tool called EditEx that predicts and helps prevent rejected edits on Stack Overflow, improving user experience and content quality by reducing rejected suggestions.
Contribution
It introduces a novel prediction model for rejected edits, a catalog of rejection reasons, and an online tool that supports users during editing to decrease rejection rates.
Findings
EditEx predicts rejected edits with around 70% accuracy.
Using EditEx can prevent nearly half of rejected edits in practice.
Participants found EditEx's rejection reasons influential and workload reduced.
Abstract
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…
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Taxonomy
TopicsSoftware Engineering Research · Topic Modeling · Software Engineering Techniques and Practices
