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ICSE 2021

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, Anita Sarma

DOI 10.1109/icse43902.2021.00128dblp

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.

BibTeX
@inproceedings{Chatterjee-al:ICSE21,
  author    = {Amreeta Chatterjee and
               Mariam Guizani and
               Catherine Stevens and
               Jillian Emard and
               Mary Evelyn May and
               Margaret Burnett and
               Iftekhar Ahmed and
               Anita Sarma},
  title     = {{AID:} An automated detector for gender-inclusivity bugs in {OSS} project pages},
  booktitle = {ICSE},
  pages     = {1423--1435},
  publisher = {{IEEE}},
  year      = {2021},
}

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