  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.
