Investigating the Effects of Gender Bias on GitHub

Imtiaz, Nasif; Middleton, Justin; Chakraborty, Joymallya; Robson, Neill; Bai, Gina R.; Murphy-Hill, Emerson

doi:10.1109/icse.2019.00079

Cited by 76 publications

(39 citation statements)

References 32 publications

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“…However, our controlled experiment, in which patch qualities are actually equal, rules out that explanation here. Dual formulations (e.g., women-authored Pull Requests may be of higher quality) are also ruled out by our post-survey data (Section 5.4) as well as previous studies [43]. We thus hypothesize that the observed differences result from systematic biases.…”

Section: Discussion Of Resultssupporting

confidence: 50%

“…Note that our results do not support any inferences about whether men or women are more accurate at code review. Regardless of the direction of the bias, the code review process overall benefits by identifying and mitigating it [12,27,37,40,43,73,78,91,99,105]. Humans tend to claim no differences between men and women as code reviewers.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…For example, large-scale analyses of GitHub Pull Request data found that women's acceptance rate is higher than men's when their gender is not identifiable, but the trend reverses when women show their gender in their profiles [91]. Similarly, another study using behavioral data on GitHub found that women concentrate their efforts on fewer projects and exhibit a narrower band of accepted behavior [43]. Furthermore, research has shown that developers may not even recognize the potential effects of biases of code authors when performing code reviews [27,91].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have considered these goals pairwise, but not all simultaneously. For example, there have been behavioral studies in both computer science and psychology on biases (e.g., [1,43,91]), medical imaging studies of biases in psychology (e.g., [14,33]), eye-tracking studies of biases [27], and eye-tracking [71,85] and medical imaging studies [26,41,88] of other factors in computer science. However, to the best of our knowledge, we present the first experimentallycontrolled study investigating biases in computing activities by measuring multiple neurophysiological modalities.…”

Section: Introductionmentioning

confidence: 99%

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Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Huang

Leach

Sharafi

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Code review is a critical step in modern software quality assurance, yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code, but no consensus understanding has emerged. Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behavioral, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias. CCS CONCEPTS • Software and its engineering → Collaboration in software development; • Human-centered computing → Empirical studies in collaborative and social computing.

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Section: Discussion Of Resultssupporting

confidence: 50%

Section: Discussion Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Huang

Leach

Sharafi

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

View full text Add to dashboard Cite

show abstract

“…The authors found that a female developer is less likely to get her pull requests accepted when her gender becomes explicit. Recently, Imtiaz et al further examined the publicly visible developers' behaviors on GitHub to tested the hypotheses on the effects of these biases on female developers' participation [42]. By analyzing the dynamics of female developers' activities, Wang et al provided an alternative explanation for females' low participation in the lens of the competence-confidence gap [83].…”

Section: Gender Issues In Software Engineeringmentioning

confidence: 99%

Reducing implicit gender biases in software development: does intergroup contact theory work?

Wang¹,

Zhang

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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The software development profession suffers from severe gender biases, which could be explicit and implicit. However, SE literature has not systematically explored and evaluated the methods for reducing gender biases, especially for implicit gender biases. This paper reports on a field experiment to examine whether the intergroup contact theory could reduce implicit gender biases in software development. In the field experiment, 280 undergraduate students taking a project-centric introductory software engineering course were assigned to 70 teams with different contact configurations. We measured and compared their explicit and implicit gender biases before and after contacts in their teams. The study yields a rich set of findings. First, we confirmed the positive effects of intergroup contact theory in reducing gender biases, particularly the implicit gender biases in both general and SE-specific contexts. We further revealed that such effects were subjected to different contact configurations. The intergroup contact theory's effects were maximized in teams where the number of females is greater than or equal to the number of males. When the female is the minority group in a team, contacts among members contribute to reducing male members' implicit gender biases but fail to result in the same scale of effects on female members' implicit gender biases. The findings provide insights into using intergroup contact theory in reducing implicit gender biases in software development contexts. CCS CONCEPTS • Social and professional topics → Women; • Human-centered computing → Empirical studies in collaborative and social computing.

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Where Are Females in OSS Projects? Socio Technical Interactions

Asri

Kerzazi

2019

Collaborative Networks and Digital Transformation

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Recent researches provide evidence that women are underrepresented in the field of computer science. It has been reported that less than 10% of Open Source Software (OSS) contributors in GitHub are women. Although related qualitative and quantitative studies point out the gender gap, the technical and social interaction of females within OSS still remain unexplored and largely misunderstood. As a first step towards proposing articulated actions towards diversity and inclusion, we need first to explore the gender gap in terms of activities and interactions. Thus, we propose to answer the questions: where are females in OSS projects? How they evolve? and How they contribute to the sustainability of the OSS social capital?. We particularly focus on building sociotechnical networks and analyze them to explain how females contribute and interact in practice. We reflect on interactions' graphs and examine through a preliminary study, using data from six OSS projects, possible links between existing findings and the directions we suggest for more gender diversity. We found that females are extremely underrepresented within OSS communities, but when they participate they are productive just as males, they evolve following relatively the same patterns than males and remain more involved in projects than males.

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Investigating the Effects of Gender Bias on GitHub

Cited by 76 publications

References 32 publications

Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Reducing implicit gender biases in software development: does intergroup contact theory work?

Where Are Females in OSS Projects? Socio Technical Interactions

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