GenderMag Improves Discoverability in the Field, Especially for Women: An Multi-Year Case Study of Suggest Edit, a Code Review Feature

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.