Affiliation:
1. Wayne State University
Abstract
Toxic conversations during software development interactions may have serious repercussions on a Free and Open Source Software (FOSS) development project. For example, victims of toxic conversations may become afraid to express themselves, therefore get demotivated, and may eventually leave the project. Automated filtering of toxic conversations may help a FOSS community maintain healthy interactions among its members. However, off-the-shelf toxicity detectors perform poorly on a software engineering dataset, such as one curated from code review comments. To counter this challenge, we present
ToxiCR
, a supervised learning based toxicity identification tool for code review interactions. ToxiCR includes a choice to select one of the 10 supervised learning algorithms, an option to select text vectorization techniques, eight preprocessing steps, and a large-scale labeled dataset of 19,651 code review comments. Two out of those eight preprocessing steps are software engineering domain specific. With our rigorous evaluation of the models with various combinations of preprocessing steps and vectorization techniques, we have identified the best combination for our dataset that boosts 95.8% accuracy and an 88.9% F1-score in identifying toxic texts. ToxiCR significantly outperforms existing toxicity detectors on our dataset. We have released our dataset, pre-trained models, evaluation results, and source code publicly, which is available at
https://github.com/WSU-SEAL/ToxiCR
.
Publisher
Association for Computing Machinery (ACM)
Reference95 articles.
1. GitHub. 2018. Annotation Instructions for Toxicity with Sub-Attributes. Retrieved February 18 2023 from https://github.com/conversationai/conversationai.github.io/blob/main/crowdsourcing_annotation_schemes/toxicity_with_subattributes.md.
2. Kaggle. 2018. Toxic Comment Classification Challenge. Retrieved February 18 2023 from https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge.
3. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 2016. TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 265–283.
4. DocBERT: BERT for document classification;Adhikari Ashutosh;arXiv preprint arXiv:1904.08398,2019
5. Sonam Adinolf and Selen Turkay. 2018. Toxic behaviors in Esports games: Player perceptions and coping strategies. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts. 365–372.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Incivility detection in open source code review and issue discussions;Journal of Systems and Software;2024-03
2. Predicting open source contributor turnover from value-related discussions: An analysis of GitHub issues;Proceedings of the 46th IEEE/ACM International Conference on Software Engineering;2024-02-06
3. ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments;2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM);2023-10-26