This is one of the findings of research carried out in the US by the computer science department of Cal Poly, San Luis Obispo and North Carolina State University.
They compared acceptance rates of contributions from men versus women in the GitHub open source software community.
The researchers found that women contributors to the GitHub community were more competent at writing good code. But there was still evidence of bias against women developers.
“Surprisingly, our results show that women’s contributions tend to be accepted more often than men’s. However, when a woman’s gender is identifiable, they are rejected more often,” said the researchers’ in their paper: “Gender Bias in Open Source: Pull Request Acceptance of Women Versus Men”.
The researchers studied how software developers respond to pull requests, proposed changes to a software project’s code, documentation, or other resources, on GitHub, which is one of the largest open source communities.
They wanted to find out whether pull requests are accepted at different rates for self-identified women compared to self-identified men.
They were a little surprised by the result that women were more likely to have their pull requests accepted at a higher rate than men.
What could explain this unexpected result?
The research looked at a number of contributory factors.
The conclusion was that women developers make more significant changes to open source code on GitHub and they show high competence across different programming languages.
However, the finding indicated that when women contributors identified their gender they found it more difficult to get their code changes accepted by the open source community.
The researchers wrote:
“Women make pull requests that add and remove more lines of code, modify more files, and contain more commits. Overall, we observe that women’s acceptance rates dominate over men’s for every programming language in the top ten, to various degrees.”
But “we see evidence for gender bias: women’s acceptance rates are 71.8% when they use gender neutral profiles, but drop to 62.5% when their gender is identifiable.”
The research findings was published in paper: “Gender Bias in Open Source: Pull Request Acceptance of Women Versus Men” by Josh Terrell from CalPoly with Andrew Kofink, Justin Middleton, Clarissa Rainear, Emerson Murphy-Hill and Chris Parnin from North Carolina State University.