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The data collected from open source projects provide means to model large software ecosystems, but often suffer from data quality issues, specifically, multiple author identification strings in code commits might actually be associated with one developer. While many methods have been proposed for addressing this problem, they are either heuristics requiring manual tweaking, or require too much calculation time to do pairwise comparisons for 38M author IDs in, for example, the World of Code collection. In this paper, we propose a method that finds all author IDs belonging to a single developer in this entire dataset, and share the list of all author IDs that were found to have aliases. To do this, we first create blocks of potentially connected author IDs and then use a machine learning model to predict which of these potentially related IDs belong to the same developer. We processed around 38 million author IDs and found around 14.8 million IDs to have an alias, which belong to 5.4 million different developers, with the median number of aliases being 2 per developer. This dataset can be used to create more accurate models of developer behaviour at the entire OSS ecosystem level and can be used to provide a service to rapidly resolve new author IDs. 

Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality it is desirable to identify bots in order to separate their actions from actions of individuals. Aim: Find an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns. Method and Result: We propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the ommits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of whom have more than 1000 commits) and 13,762,430 commits they created.