AID: An Automated Detector for Gender-Inclusivity Bugs in OSS Project Pages

Amreeta Chatterjee, M. Guizani, Catherine Stevens, Jillian Emard, Mary Evelyn May, M. Burnett, Iftekhar Ahmed, A. Sarma
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引用次数: 9

Abstract

The tools and infrastructure used in tech, including Open Source Software (OSS), can embed "inclusivity bugs"- features that disproportionately disadvantage particular groups of contributors. To see whether OSS developers have existing practices to ward off such bugs, we surveyed 266 OSS developers. Our results show that a majority (77%) of developers do not use any inclusivity practices, and 92% of respondents cited a lack of concrete resources to enable them to do so. To help fill this gap, this paper introduces AID, a tool that automates the GenderMag method to systematically find gender-inclusivity bugs in software. We then present the results of the tool's evaluation on 20 GitHub projects. The tool achieved precision of 0.69, recall of 0.92, an F-measure of 0.79 and even captured some inclusivity bugs that human GenderMag teams missed.
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AID: OSS项目页面中性别包容性错误的自动检测器
科技领域使用的工具和基础设施,包括开源软件(OSS),都可能嵌入“包容性缺陷”——这些特征会对特定的贡献者群体造成不成比例的不利影响。为了了解OSS开发人员是否有现有的实践来避免此类错误,我们调查了266名OSS开发人员。我们的结果显示,大多数(77%)开发者没有使用任何包容性实践,92%的受访者表示缺乏具体的资源来帮助他们这样做。为了帮助填补这一空白,本文介绍了AID,这是一个自动化GenderMag方法的工具,可以系统地发现软件中的性别包容性错误。然后,我们展示了该工具对20个GitHub项目的评估结果。该工具的精确度为0.69,召回率为0.92,f值为0.79,甚至还捕获了一些人类《性别杂志》团队遗漏的包容性错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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