Seeking new measures for gender bias effects in open-source software
Huilian Sophie Qiu, Moira Connell · 2022
The problem of low gender diversity in open-source software (OSS) has been reported and studied in recent years. However, prior studies found that gender bias theories in social sciences cannot help us effectively identify gender bias effects in OSS. Our study takes the first step toward finding new measures for gender bias in OSS. This paper attempts to employ linguistic theories to identify different collaboration patterns between different genders. Our contributions are two-fold: we review linguistic literature on diversity and online collaboration, then we apply linguistic theories from our literature reviews to a random sample of code review conversations on GitHub.