  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 to
maintain healthy interactions among its members. However, off-the-shelf
toxicity detectors perform poorly on Software Engineering (SE) datasets, such
as one curated from code review comments. To encounter 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 ten
supervised learning algorithms, an option to select text vectorization
techniques, eight preprocessing steps, and a large-scale labeled dataset of
19,571 code review comments. Two out of those eight preprocessing steps are SE
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
88.9% F1 score. ToxiCR significantly outperforms existing toxicity detectors on
our dataset. We have released our dataset, pre-trained models, evaluation
results, and source code publicly available at:
https://github.com/WSU-SEAL/ToxiCR
