Real-Time Toxicity Filtering for Open-Source Code Reviews
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908951067492352 |
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| author | Anindya, Md Awsaf Alam Biswas, Showvik Iqbal, Anindya Sarker, Jaydeb Bosu, Amiangshu |
| author_facet | Anindya, Md Awsaf Alam Biswas, Showvik Iqbal, Anindya Sarker, Jaydeb Bosu, Amiangshu |
| contents | Toxic interactions in open-source software development harm community collaboration. To combat this, we propose ToxiShield, a realtime browser extension that identifies and detoxifies toxic code reviews. The framework comprises three modules: toxicity identification, reasoned multiclass classification, and code review detoxification. Our fine-tuned BERT-based binary classifier achieved a 97% F1-score on 38,761 code review texts. For multiclass classification, Claude 3.5 Sonnet with prompt engineering achieved a 39% MCC and 42% F1 on 1,200 samples. Finally, our fine-tuned Llama 3.2 detoxification model reached 95.27% style transfer accuracy, 97.03% fluency, 67.07% content preservation, and an 84% J-score. Validation with 10 software developers suggests ToxiShield effectively fosters a more inclusive open-source environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-Time Toxicity Filtering for Open-Source Code Reviews Anindya, Md Awsaf Alam Biswas, Showvik Iqbal, Anindya Sarker, Jaydeb Bosu, Amiangshu Software Engineering Toxic interactions in open-source software development harm community collaboration. To combat this, we propose ToxiShield, a realtime browser extension that identifies and detoxifies toxic code reviews. The framework comprises three modules: toxicity identification, reasoned multiclass classification, and code review detoxification. Our fine-tuned BERT-based binary classifier achieved a 97% F1-score on 38,761 code review texts. For multiclass classification, Claude 3.5 Sonnet with prompt engineering achieved a 39% MCC and 42% F1 on 1,200 samples. Finally, our fine-tuned Llama 3.2 detoxification model reached 95.27% style transfer accuracy, 97.03% fluency, 67.07% content preservation, and an 84% J-score. Validation with 10 software developers suggests ToxiShield effectively fosters a more inclusive open-source environment. |
| title | Real-Time Toxicity Filtering for Open-Source Code Reviews |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.08886 |