Gender Bias in Open Source Software Development: Analyzing 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.

Research on gender bias in open source software development reveals that women’s pull requests are accepted at a higher rate than men’s overall, but this trend reverses when their gender is identifiable, particularly for outsiders. The study utilized historical GitHub data to analyze over 1.4 million contributors, finding that women tend to submit larger and more complex changes. Despite experiencing bias in the broader tech community, women on GitHub appear more competent, which may contribute to their higher acceptance rates. However, the results highlight the existence of underlying gender biases, suggesting that while women may excel in some areas, they still face discrimination when identifiable.

Women’s pull requests tend to have a higher acceptance rate than men’s (78.7% vs. 74.6%).
Acceptance rates drop for identifiable women outsiders compared to men, indicating bias.
Women generally submit larger pull requests with more lines of code than men.
The findings challenge the notion that open source is a purely meritocratic space.

What were the main findings regarding acceptance rates of pull requests based on gender?

Women’s pull requests are accepted at a higher rate than men’s overall, but identifiable women outsiders experience lower acceptance rates compared to identifiable men.

How did the researchers analyze the data?

The study analyzed historical GitHub data, linking users to their self-reported genders through social media profiles, allowing for a large-scale investigation of pull request acceptance rates.

What does the study suggest about gender bias in open source software development?

The study indicates that while women may demonstrate higher competence in their contributions, they still face gender bias, particularly when their gender is identifiable, challenging the perception of open source as a meritocratic environment.