一句话：通过五年日志与数万用户数据，首次大规模实证 GenderMag 能提升代码评审工具的可发现性并实现性别平等。
先前研究表明 GenderMag 方法有助于识别和解决更易影响女性软件用户的可用性障碍，但有效性证据仅限于小型实验室研究。本文开展案例研究，将 Google 内部代码评审工具数万名用户的自报性别数据与五年间收集的软件日志相结合，进行定量分析。结果表明，GenderMag 帮助 Google 某团队（a）正确识别出可发现性是一种更易影响女性而非男性的可用性障碍，并且（b）将可发现性提升 2.4 倍，同时实现性别平等。具体而言，与使用原始代码评审工具的男性相比，使用 GenderMag 重新设计系统的女性和男性在任何给定时刻发现“Suggest Edit”功能的可能性都高出 2.4 倍。本文贡献了 GenderMag 在实地中有效性的首个大规模证据。
Prior research shows that the GenderMag method can help identify and address usability barriers that are more likely to affect women software users than men. However, the evidence for the effectiveness of GenderMag is limited to small lab studies. In this case study, by combining self-reported gender data from tens of thousands of users of an internal code review tool with software logs data gathered over a five-year period, we quantitatively show that GenderMag helped a team at Google (a) correctly identify discoverability as a usability barrier more likely to affect women than men, and (b) increase discoverability by 2.4x while also achieving gender parity. That is, compared to men using the original code review tool, women and men using the system redesigned with GenderMag were both 2.4x more likely to discover the "Suggest Edit" feature at any given time. Thus, this paper contributes the first large-scale evidence of the effectiveness of GenderMag in the field.
@inproceedings{paperbot11402,
title = {GenderMag Improves Discoverability in the Field, Especially for Women},
author = {Emerson Murphy-Hill and Alberto Elizondo and Ambar Murillo and Marian Harbach and Bogdan Vasilescu and Delphine Carlson and Florian Dessloch},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639097}
}