Collaborative software development through GitHub repositories frequently relies on bot accounts to automate repetitive and error-prone tasks. This highlights the need to have accurate and efficient bot identification tools. Several such tools have been proposed in the past, but they tend to rely on a substantial amount of historical data, or they limit themselves to a reduced subset of activity types, making them difficult to use at large scale. To overcome these limitations, we developed RABBIT, an open source command-line tool that queries the GitHub Events API to retrieve the recent events of a given GitHub account and predicts whether the account is a human or a bot. RABBIT is based on an XGBoost classification model that relies on six features related to account activities and achieves high performance, with an AUC, F1 score, precision and recall of 0.92. Compared to the state-of-the-art in bot identification, RABBIT exhibits a similar performance in terms of precision, recall and F1 score, while being more than an order of magnitude faster and requiring considerably less data. This makes RABBIT usable on a large scale, capable of processing several thousand accounts per hour efficiently.

Software repositories hosted on GitHub frequently use development bots to automate repetitive, effort intensive and error-prone tasks. To understand and study how these bots are used, state-of-the-art bot identification tools have been developed to detect bots based on their comments in commits, issues and pull requests. Given that bots can be involved in many other activity types, there is a need to consider more activities that they are carrying out in the software repositories they are involved in. We therefore propose a curated dataset of such activities carried out by bots and humans involved in GitHub repositories. The dataset was constructed by identifying 24 high-level activity types that could be extracted from 15 lower-level event types that were queried from GitHub’s event stream API for all considered bots and humans. The proposed dataset contains around 834K activities performed by 385 bots and 616 humans involved in GitHub repositories, during an observation period ranging from 25 November 2022 to 9 March 2023. By analysing the activity patterns of bots and humans, this dataset could lead to better bot identification tools and empirical studies on how bots play a role in collaborative software development.

Contemporary social coding platforms like GitHub promote collaborative development. Many open-source software repositories hosted in these platforms use machine accounts (bots) to automate and facilitate a wide range of effort-intensive and repetitive activities. Determining if an account corresponds to a bot or a human contributor is important for socio-technical development analytics, for example, to understand how humans collaborate and interact in the presence of bots, to assess the positive and negative impact of using bots, to identify the top project contributors, to identify potential bus factors, and so on. Our project aims to include the trained machine learning (ML) classifier from the BoDeGHa bot detection tool as a plugin to the GrimoireLab software development analytics platform. In this work, we present the procedure to form a pipeline for retrieving contribution and contributor data using Perceval, distinguishing bots from humans using BoDeGHa, and visualising the results using Kibana.