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ToxiCR

A supervised learning based tool to identify toxic code review comments

Read Our Papers

#[TOSEM-23: "Automated Identification of Toxic Code Reviews Using ToxiCR", https://dl.acm.org/doi/10.1145/3583562]

#[ASE 2022 (Short paper appeared in SRP): "‘Who built this crap?’ Developing a Software Engineering Domain Specific Toxicity Detector" https://dl.acm.org/doi/10.1145/3551349.3559508]

Datasets

Our code review dataset is: /models/code-review-dataset-full.xlsx

Our dataset contains total 19,651 labeled CR comments. Each comments is labeled as toxic (1) or non-toxic (0). Finally, the number of toxic comments is 3757 (~19% of the dataset).

Instructions to use the tool

Clone/download the project

git clone https://github.com/WSU-SEAL/ToxiCR.git

Install all the requirements:

pip install -r "requirements.txt"

Now, you can view all the options for ToxiCR.py file using:

python ToxiCR.py --h

You can select any mode after: python ToxiCR.py (put arguments)

Retrain mode:

To retrain the best performing BERT model (with keyword preprocessing):

python ToxiCR.py --algo BERT --embed bert --keyword --mode pretrain

Test Mode:

python ToxiCRTest.py

Copyright Information

Copyright Software Engineering Analytics Lab (SEAL), Wayne State University, 2022 Authors: Jaydeb Sarker [email protected] and Amiangshu Bosu [email protected]

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Citation for our papers

If you use our work, please cite our paper:

TOSEM-23: "Automated Identification of Toxic Code Reviews Using ToxiCR"

@article{10.1145/3583562,
author = {Sarker, Jaydeb and Turzo, Asif Kamal and Dong, Ming and Bosu, Amiangshu},
title = {Automated Identification of Toxic Code Reviews Using ToxiCR},
year = {2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1049-331X},
url = {https://doi.org/10.1145/3583562},
doi = {10.1145/3583562},
note = {Just Accepted},
journal = {ACM Trans. Softw. Eng. Methodol.},
month = {feb},
keywords = {natural language processing, tool development, toxicity, sentiment analysis, code review}
}

ASE 2022 (Short paper): "‘Who built this crap?’ Developing a Software Engineering Domain Specific Toxicity Detector"

@inproceedings{10.1145/3551349.3559508,
author = {Sarker, Jaydeb},
title = {‘Who Built This Crap?’ Developing a Software Engineering Domain Specific Toxicity Detector},
year = {2023},
isbn = {9781450394758},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3551349.3559508},
doi = {10.1145/3551349.3559508},
booktitle = {Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering},
articleno = {200},
numpages = {3},
keywords = {toxicity, developers interaction, NLP, deep learning},
location = {Rochester, MI, USA},
series = {ASE '22}
}