Github coding study suggests gender bias Published duration 12 February 2016

image copyright Getty Images image caption The researchers used data taken from the Github program-sharing platform

New research shows that software coding changes suggested by women had higher approval ratings than those from men - but only if their gender was not identifiable.

The US researchers analysed nearly 1.4 million users of the open source program-sharing service Github.

Ratings for women fell if they were not regulars on the service and were identified by their gender, suggesting a bias in that community.

The paper is awaiting peer review.

This means the results have yet to be critically appraised by other experts.

The researchers, from the computer science departments at Cal Poly, San Luis Obispo, and North Carolina State University, looked at around four million people who logged on to Github on a single day - 1 April 2015.

Github is an enormous developer community which does not request gender information from its 12 million users.

However the team was able to identify whether roughly 1.4m were male or female - either because it was clear from the users' profiles or because their email addresses could be matched with the Google + social network.

The researchers accepted that this was a privacy risk but said they did not intend to publish the raw data.

image copyright Getty Images image caption The researchers found women fared better if their gender was not clear

The team found that 78.6% of pull requests made by women were accepted compared with 74.6% of those by men.

The researchers considered various factors, such as whether women were more likely to be responding to known issues, whether their contributions were shorter in length and so easier to appraise, and which programming language they were using, but they could not find a correlation.

However among users who were not well known within the community, those whose profiles made clear that they were women had a much lower acceptance rate than those whose gender was not obvious.

'Bias nonetheless'

"For outsiders, 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 . There is a similar drop for men, but the effect is not as strong," the paper noted.

"Women have a higher acceptance rate of pull requests overall, but when they're outsiders and their gender is identifiable, they have a lower acceptance rate than men.

"Our results suggest that although women on Github may be more competent overall, bias against them exists nonetheless," the researchers concluded.

image copyright Isis Anchalee image caption Developer Isis Anchalee started a social media campaign last year when people questioned her career

Despite various high profile initiatives, tech firms continue to face challenges in terms of the diversity of their staff, in terms of both gender and ethnicity, particularly in more technical careers.

However the researchers' findings are still encouraging, computer scientist Dr Sue Black OBE told the BBC.

"I think we are going to see a resurgence of interest from women in not only coding but all sorts of tech-related careers over the next few years," she said.

"Knowing that women are great at coding gives strength to the case that it's better for everyone to have more women working in tech.

"It was a woman - Ada Lovelace - who came up with the idea of software in the first place, we owe it to her to make sure that we encourage and support women into the software industry," Dr Black added.