Given that it seems like they analysed real-world data rather than producing synthetic experiments, I wouldn't put too much stock in the findings. For instance, in a hypothetical world where all businesses contributing to Github projects are perfectly sexist, only ever assign male coders to core engineering and only ever assign female coders to UX design, UX design is intrinsically more controversial and Github maintainerers accept patches purely on the degree of public support for their contents, one would expect a far lower degree of acceptance for patches from identifiable (as businesses presumably make their workers operate under real-name accounts) female accounts without this containing any signal about sexism on the Github side.

A better methodology for a study like this would be to identify a number of people planning to make sporadic contributions to projects they have no connection to and making them submit the pull requests under fresh fictional profiles with randomised gender. This way, any irrelevant correlations between the kind of pull request being made and the requesting party being identifiable as female would be eliminated.