Evaluating a bot detection model on git commit messages
This work addresses bias prevention in socio-technical empirical analyses for software engineering researchers, but it is incremental as it extends an existing method to a new data type.
The study tackled the problem of detecting bots in distributed software development by generalizing a classification model from pull request and issue comments to git commit messages, resulting in an increase in precision from 0.77 to 0.80 on a dataset of 6,922 git contributors.
Detecting the presence of bots in distributed software development activity is very important in order to prevent bias in large-scale socio-technical empirical analyses. In previous work, we proposed a classification model to detect bots in GitHub repositories based on the pull request and issue comments of GitHub accounts. The current study generalises the approach to git contributors based on their commit messages. We train and evaluate the classification model on a large dataset of 6,922 git contributors. The original model based on pull request and issue comments obtained a precision of 0.77 on this dataset. Retraining the classification model on git commit messages increased the precision to 0.80. As a proof-of-concept, we implemented this model in BoDeGiC, an open source command-line tool to detect bots in git repositories.