AID: An Automated Detector for Gender-Inclusivity Bugs in OSS Project Pages
Amreeta Chatterjee, Mariam Guizani, Catherine Stevens, Jillian Emard, Mary Evelyn May, Margaret Burnett, Iftekhar Ahmed
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.