How Gender-Biased Tools Shape Newcomer Experiences in OSS Projects (original) (raw)
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Work Practices and Perceptions from Women Core Developers in OSS Communities
Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)
Background. The effect of gender diversity in open source communities has gained increasing attention from practitioners and researchers. For instance, organizations such as the Python Software Foundation and the OpenStack Foundation started actions to increase gender diversity and promote women to top positions in the communities. Problem. Although the general underrepresentation of women (a.k.a. horizontal segregation) in open source communities has been explored in a number of research studies, little is known about the vertical segregation in open source communitieswhich occurs when there are fewer women in high level positions. Aims. To address this research gap, in this paper we present the results of a mixed-methods study on gender diversity and work practices of core developers contributing to open-source communities. Method. In the first study, we used mining-software repositories procedures to identify the core developers of 711 open source projects, in order to understand how common are women core developers in open source communities and characterize their work practices. In the second study, we surveyed the women core developers we identified in the first study to collect their perceptions of gender diversity and gender bias they might have observed while contributing to open source systems. Results. Our findings show that open source communities present both horizontal and vertical segregation (only 2.3% of the core developers are women). Nevertheless, differently from previous studies, most of the women core developers (65.7%) report never having experienced gender discrimination when contributing to an open source project. Finally, we did not note substantial differences between the work practices among women and men core developers. Conclusions. We reflect on these findings and present some ideas that might increase the participation of women in open source communities.
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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 twofold: 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. 1 INTRODUCTION The low gender diversity in the open-source software (OSS) community is a well-known phenomenon: among the GitHub users whose genders can be inferred, less than 10% are women [1, 6, 15, 30]. The low gender diversity is problematic as it can threaten OSS sustainability as a whole. Firstly, low gender diversity is suboptimal for project success: studies found that higher gender diversity is associated with fewer community smells [7, 38] and higher team performance [26, 34, 40]. Moreover, the highly imbalanced gender representation and the unwelcoming culture in some open-source projects [23] may discourage underrepresented groups from initial participation, which limits opportunities both for those individuals and for employers that use OSS as a talent pool [32, 33]. One of the reasons for women's low participation is gender bias [19, 23, 39]. Based on interviews with OSS developers, Nafus [23] pointed out that, in OSS, "sexist behavior is [...] as constant as it is extreme. " A quantitative study by Terrell et al. [39] reports that female contributors face unfair treatments when making code contributions. This piece of work builds upon a prior attempt on investigating gender bias effects in OSS by Imtiaz et al. [19]. In their paper, Imtiaz et al. adapted a gender bias framework by Williams and Dempsey [42], which was developed for women in the workforce, to the context of OSS. The framework discusses four effects of gender bias women may face in the workforce. Prove-It-Again: women
Open source barriers to entry, revisited
Proceedings of the 40th International Conference on Software Engineering, 2018
Research has revealed that significant barriers exist when entering Open-Source Software (OSS) communities and that women disproportionately experience such barriers. However, this research has focused mainly on social/cultural factors, ignoring the environment itself-the tools and infrastructure. To shed some light onto how tools and infrastructure might somehow factor into OSS barriers to entry, we conducted a field study with five teams of software professionals, who worked through five use-cases to analyze the tools and infrastructure used in their OSS projects. These software professionals found tool/infrastructure barriers in 7% to 71% of the use-case steps that they analyzed, most of which are tied to newcomer barriers that have been established in the literature. Further, over 80% of the barrier types they found include attributes that are biased against women.