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Development bots are used on Github to automate repetitive activities. Suchbots communicate with human actors via issue comments and pull requestcomments. Identifying such bot comments allows preventing bias insocio-technical studies related to software development. To automate theiridentification, we propose a classification model based on natural languageprocessing. Starting from a balanced ground-truth dataset of 19,282 PR andissue comments, we encode the comments as vectors using a combination of thebag of words and TF-IDF techniques. We train a range of binary classifiers topredict the type of comment (human or bot) based on this vector representation.A multinomial Naive Bayes classifier provides the best results. Its performanceon a test set containing 50% of the data achieves an average precision, recall,and F1 score of 0.88. Although the model shows a promising result on the pullrequest and issue comments, further work is required to generalize the model onother types of activities, like commit messages and code reviews.

Identifying bot activity in GitHub pull request and issue comments