AID: An automated detector for gender-inclusivity bugs in OSS project pages

Public Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/defaults/ws859n93g

Descriptions

Attribute Name	Values
Creator	Chatterjee, Amreeta Guizani, Mariam Stevens, Catherine Emard, Jillian May, Mary Evelyn Burnett, Margaret Ahmed, Iftekhar Sarma, Anita
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. Index Terms—Gender inclusivity, automation, open source, information processing
Resource Type	Conference Proceeding
Date Issued	2021
Conference Name	International Conference on Software Engineering (ICSE)
Conference Section/Track	Technical
Conference Location	Virtual
Academic Affiliation	Electrical Engineering and Computer Science
Rights Statement	In Copyright
Related Items	ICSE 2021
Publisher	Oregon State University
Language	English [eng]

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