Automatic prediction of rejected edits in Stack Overflow

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 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.

Reproducibility of Issues Reported in Stack Overflow Questions: Challenges, Impact & Estimation

“…Therefore, we chose five popular supervised ML classifiers with different learning strategies to classify the issue's reproducible/irreproducible status. These algorithms are selected because (1) they can build reliable models to predict reproducibility status using our features, and (2) they are widely used in relevant studies [51,52,53,54,50]. Random Forest (RF) is a popular supervised ML technique due to its simplicity and versatility [55].…”

“…RF scales well to any number of dimensions and offers acceptable performance. Instead of searching for the most important feature when splitting nodes, it looks for the best feature within a random subset [51], which helps prevent model overfitting. eXtreme Gradient Boosting (XGBoost) is a scalable tree-boosting technique [57] that predicts a target variable by combining estimates from simpler weak models.…”

“…eXtreme Gradient Boosting (XGBoost) is a scalable tree-boosting technique [57] that predicts a target variable by combining estimates from simpler weak models. It works for classification and regression and offers improved speed and performance compared to traditional gradient-boosted decision trees (GBM) [51]. XGBoost supports parallel and distributed computing, making it faster than other algorithms, and handles different data types and relationships well.…”