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Background: The existence of toxic conversations in open-source platforms candegrade relationships among software developers and may negatively impactsoftware product quality. To help mitigate this, some initial work has beendone to detect toxic comments in the Software Engineering (SE) domain. Aims:Since automatically classifying an entire text as toxic or non-toxic does nothelp human moderators to understand the specific reason(s) for toxicity, weworked to develop an explainable toxicity detector for the SE domain. Method:Our explainable toxicity detector can detect specific spans of toxic contentfrom SE texts, which can help human moderators by automatically highlightingthose spans. This toxic span detection model, ToxiSpanSE, is trained with the19,651 code review (CR) comments with labeled toxic spans. Our annotatorslabeled the toxic spans within 3,757 toxic CR samples. We explored severaltypes of models, including one lexicon-based approach and five differenttransformer-based encoders. Results: After an extensive evaluation of allmodels, we found that our fine-tuned RoBERTa model achieved the best score with0.88 $F1$, 0.87 precision, and 0.93 recall for toxic class tokens, providing anexplainable toxicity classifier for the SE domain. Conclusion: Since ToxiSpanSEis the first tool to detect toxic spans in the SE domain, this tool will pave apath to combat toxicity in the SE community.

ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments