The GitHub social coding platform introduced GitHub Actions as a way to automate different aspects of collaborative software development through the use of workflow files. It is the most popular CI/CD and workflow automation tool for GitHub. To maintain workflow code over time, it is useful to rely on differencing tools to identify the changes made during successive commits. Unfortunately, existing code differencing tools are not able to correctly identify changes made to workflow files. We therefore implemented gawd, a syntactic differencing tool for GitHub Actions workflows. The tool is capable of reporting the addition, deletion, modification and move of syntactic components in workflow files, taking into account the specific syntax of workflows. gawd has been evaluated on manually classified sets of workflow changes taken from existing commits in 40 different GitHub repositories, and was able to successfully identify these changes. gawd is publicly released as an open source Python tool distributed on PyPI.

GitHub Actions was introduced in 2019 and constitutes an integrated alternative to CI/CD services for GitHub repositories. The deep integration with GitHub allows repositories to easily automate software development workflows. This paper empirically studies the use of GitHub Actions on a dataset comprising 68K repositories on GitHub, of which 43.9% are using GitHub Actions workflows. We analyse which workflows are automated and identify the most frequent automation practices. We show that reuse of actions is a common practice, even if this reuse is concentrated in a limited number of actions. We study which actions are most frequently used and how workflows refer to them. Furthermore, we discuss the related security and versioning aspects. As such, we provide an overview of the use of GitHub Actions, constituting a necessary first step towards a better understanding of this emerging ecosystem and its implications on collaborative software development in the GitHub social coding platform.

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.

An issue report documents the discussions around required changes in issue-tracking systems, while a commit contains the change itself in the version control systems. Recovering links between issues and commits can facilitate many software evolution tasks such as bug localization, defect prediction, software quality measurement, and software documentation. A previous study on over half a million issues from GitHub reports only about 42.2% of issues are manually linked by developers to their pertinent commits. Automating the linking of commit-issue pairs can contribute to the improvement of the said tasks. By far, current state-of-the-art approaches for automated commit-issue linking suffer from low precision, leading to unreliable results, sometimes to the point that imposes human supervision on the predicted links. The low performance gets even more severe when there is a lack of textual information in either commits or issues. Current approaches are also proven computationally expensive. We propose Hybrid-Linker, an enhanced approach that overcomes such limitations by exploiting two information channels; (1) a non-textual-based component that operates on non-textual, automatically recorded information of the commit-issue pairs to predict a link, and (2) a textual-based one which does the same using textual information of the commit-issue pairs. Then, combining the results from the two classifiers, Hybrid-Linker makes the final prediction. Thus, every time one component falls short in predicting a link, the other component fills the gap and improves the results. We evaluate Hybrid-Linker against competing approaches, namely FRLink and DeepLink on a dataset of 12 projects. Hybrid-Linker achieves 90.1%, 87.8%, and 88.9% based on recall, precision, and F-measure, respectively. It also outperforms FRLink and DeepLink by 31.3%, and 41.3%, regarding the F-measure. Moreover, the proposed approach exhibits extensive improvements in terms of performance as well. Finally, our source code and data are publicly available.