Software developers’ race may affect their contribution acceptance rate

Courtesy Jia Chen

University of Waterloo researchers have found that a software developer’s race and ethnicity may affect how their work is judged by others. According to the study, developers who are perceived as people of colour are less likely to see their open-source proposals accepted.

The paper, titled “On the Relationship Between the Developer’s Perceptible Race and Ethnicity and the Evaluation of Contributions in OSS,” was recently published in the journal IEEE Transactions on Software Engineering.

The study was focused on GitHub, a software platform used to host code for many open-source projects. Anyone can propose contributions to projects on GitHub, which will either be accepted or rejected by their peers. Developers can only see the name of the contributor when reviewing their work.

“A developer’s contributions to an open-source software project are accepted or rejected for a variety of technical reasons, but our analysis of tens of thousands of projects on GitHub shows that contributions can be accepted or rejected because of other factors,” said Mei Nagappan, a professor at Waterloo’s Cheriton School of Computer Science in a Guelph Today article. “We found that one of them is the perceived race and ethnicity of a developer based on the person’s name on the platform.”

Nagappan and his team used a tool called NamePrism to estimate the perceived race of developers based on their GitHub username. They analyzed 37,700 open-source projects involving nearly 366,000 developers.

The team found that roughly 70 per cent of contributions belonged to individuals perceived as white. Asian, Black and Hispanic contributors were the least likely to have their work accepted, with only 10 per cent of the accepted contributions.

“This low percentage is concerning because it does not reflect the percentage of developers among these groups in the larger tech community,” Nagappan said. “The odds of non-white developers’ contributions getting accepted are lower, but we don’t know why it may be lower.”

Nagappan said he hopes research will continue in this area, and that his findings can be used to identify diversity problems in the industry and determine how these biases can be corrected.