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Graphs of developer networks are important for software engineering researchand practice. For these graphs to realistically represent the networks,accurate developer identities are imperative. We aim to identify developeridentity errors from open source software repositories in VCS, investigate thenature of these errors, design corrective algorithms, and estimate the impactof the errors on networks inferred from this data. We investigate thesequestions using over 1B Git commits with over 23M recorded author identities.By inspecting the author strings that occur most frequently, we group identityerrors into categories. We then augment the author strings with 3 behavioralfingerprints: time-zone frequencies, the set of files modified, and a vectorembedding of the commit messages. We create a manually validated set ofidentities for a subset of OpenStack developers using an active learningapproach and use it to fit supervised learning models to predict the identitiesfor the remaining author strings in OpenStack. We compare these predictionswith a commercial effort and a leading research method. Finally, we comparenetwork measures for file-induced author networks based on corrected and rawdata. We find commits done from different environments, misspellings,organizational IDs, default values, and anonymous IDs to be the major sourcesof errors. We also find supervised learning methods to reduce errors by severaltimes in comparison to existing methods and the active learning approach to bean effective way to create validated datasets and that correction of developeridentity has a large impact on the inference of the social network. We believethat our proposed Active Learning Fingerprint Based Anti-Aliasing (ALFAA)approach will expedite research progress in the software engineering domain forapplications that depend upon graphs of developers or other social networks.

ALFAA: Active Learning Fingerprint Based Anti-Aliasing for Correcting Developer Identity Errors in Version Control Data