Gender Bias in Open Source Software Development: A Study on Pull Request Acceptance Rates

Gender differences and bias in open source: pull request acceptance of women versus men 🔗

Biases against women in the workplace have been documented in a variety of studies. This paper presents a large scale study on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women’s contributions tend to be accepted more often than men’s. However, for contributors who are outsiders to a project and their gender is identifiable, men’s acceptance rates are higher. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

This study investigates gender bias in open source software development by analyzing the acceptance rates of pull requests submitted by women and men on GitHub. Contrary to the expectation that women would face discrimination, the findings reveal that women’s pull requests are accepted at a higher rate than men’s overall. However, this trend shifts for identifiable outsiders, where men receive higher acceptance rates. The research explores various hypotheses to explain these results, including the possibility of women being more competent, the impact of survivorship bias, and the existence of gender bias in interactions. The conclusions challenge the notion of open source as a meritocracy and highlight the importance of addressing biases in the software development community.

What was the main finding of the study regarding pull request acceptance rates?

The study found that women's pull requests on GitHub are accepted at a higher rate than those of men overall, but this changes for identifiable male and female outsiders, with men having higher acceptance rates in that scenario.

How did the researchers analyze gender bias in pull request acceptance?

The researchers analyzed historical data from GitHub, linking users' email addresses to their self-reported genders on social media, which allowed them to evaluate acceptance rates of pull requests from identified men and women.

What implications do the study's findings have for the open source community?

The findings suggest that while women may be more competent contributors in open source, bias still exists, especially when their gender is identifiable. This challenges the perception of open source as a purely meritocratic space and indicates a need for the community to address gender biases.