ALFAA：基于主动学习指纹的抗锯齿，用于纠正版本控制数据中的开发人员身份错误
ALFAA: Active Learning Fingerprint Based Anti-Aliasing for Correcting Developer Identity Errors in Version Control Data
摘要：
开发人员网络图对于软件工程的研究和实践具有重要意义。为了让这些图真实地表示网络，准确的开发人员身份是必不可少的。我们的目标是从开源软件存储库中识别开发人员身份错误，调查这些错误的性质，设计纠正算法，并估计这些错误对从这些数据推断的网络的影响。我们使用超过1B的Git提交和超过23M的记录作者身份来调查这些问题。通过检查最频繁出现的作者字符串，我们将标识错误分为几类。然后，我们用3个行为指纹来增加作者字符串：时区频率，修改的文件集，以及提交消息的向量嵌入。我们使用主动学习方法为一部分OpenStack开发人员创建了一组手动验证的身份，并使用它来拟合监督学习模型，以预测OpenStack中剩余作者字符串的身份。我们将这些预测与商业努力和领先的研究方法进行比较。最后，我们比较网络措施的文件诱导作者网络的基础上校正和原始数据。我们发现来自不同环境的提交、拼写错误、组织ID、默认值和匿名ID是错误的主要来源。我们还发现，与现有方法相比，监督学习方法可以将错误减少数倍，而主动学习方法是创建验证数据集的有效方法，并且开发人员身份的纠正对社交网络的推理有很大影响。我们相信，我们提出的主动学习基于指纹的抗锯齿（ALFAA）的方法将加快研究进展，在软件工程领域的应用程序，依赖于开发人员或其他社交网络的图形。
英文摘要：
Graphs of developer networks are important for software engineering research and practice. For these graphs to realistically represent the networks, accurate developer identities are imperative. We aim to identify developer identity errors from open source software repositories in VCS, investigate the nature of these errors, design corrective algorithms, and estimate the impact of the errors on networks inferred from this data. We investigate these questions using over 1B Git commits with over 23M recorded author identities. By inspecting the author strings that occur most frequently, we group identity errors into categories. We then augment the author strings with 3 behavioral fingerprints: time-zone frequencies, the set of files modified, and a vector embedding of the commit messages. We create a manually validated set of identities for a subset of OpenStack developers using an active learning approach and use it to fit supervised learning models to predict the identities for the remaining author strings in OpenStack. We compare these predictions with a commercial effort and a leading research method. Finally, we compare network measures for file-induced author networks based on corrected and raw data. We find commits done from different environments, misspellings, organizational IDs, default values, and anonymous IDs to be the major sources of errors. We also find supervised learning methods to reduce errors by several times in comparison to existing methods and the active learning approach to be an effective way to create validated datasets and that correction of developer identity has a large impact on the inference of the social network. We believe that our proposed Active Learning Fingerprint Based Anti-Aliasing (ALFAA) approach will expedite research progress in the software engineering domain for applications that depend upon graphs of developers or other social networks.
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