Gender differences and bias in open source: Pull request acceptance of women versus men

Terrell, Josh; Middleton, Justin; Rainear, Clarissa; Murphy-Hill, Emerson; Parnin, Chris; Stallings, Jon

doi:10.7287/peerj.preprints.1733v2

Cited by 21 publications

(22 citation statements)

References 2 publications

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“…Social aspects of the contributor played a role in recent studies (Terrell et al, 2017;Ford et al, 2019, e.g. ) and we can confirm that there are at least a few consciously perceived differences from the developer's point of view as to whether the contributor is human or bot.…”

Section: Resultssupporting

confidence: 78%

“…However, pull requests in that study were proposed manually and only a single time after prior consultation with the project maintainers. Furthermore, we know that contributions are not only evaluated on their content, but also on the social characteristics of the contributor (Terrell et al, 2017;Ford et al, 2019). In the case of contributing bots, identifying them as bots can be sufficient to observe a negative bias compared to contributions from humans (Murgia et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Perception and Acceptance of an Autonomous Refactoring Bot

Wyrich

Hebig

Wagner

et al. 2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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The use of autonomous bots for automatic support in software development tasks is increasing. In the past, however, they were not always perceived positively and sometimes experienced a negative bias compared to their human counterparts. We conducted a qualitative study in which we deployed an autonomous refactoring bot for 41 days in a student software development project. In between and at the end, we conducted semistructured interviews to find out how developers perceive the bot and whether they are more or less critical when reviewing the contributions of a bot compared to human contributions. Our findings show that the bot was perceived as a useful and unobtrusive contributor, and developers were no more critical of it than they were about their human colleagues, but only a few team members felt responsible for the bot.

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Section: Resultssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Perception and Acceptance of an Autonomous Refactoring Bot

Wyrich

Hebig

Wagner

et al. 2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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“…Recently, female software developers reported not only sexual harassment [23] but also various technical biases. For example, women are often assigned menial tasks [25] and code commits from females are less likely to be accepted [65] than males. Marwick et al claims that in the current software industry, females often feel like they "do not belong" and often struggle with an "imposter syndrome" 1 [49].…”

Section: Introductionmentioning

confidence: 99%

“…For this research, we mined the code review repositories of six popular open source software (OSS) projects and identified all the developers that have committed at least five code changes for those projects. Based on the approach adopted in a recent study [65], we developed a semi-automated methodology followed by a manual validation using social networks (i.e., LinkedIn, Google Plus, Facebook, Github, and Twitter) to identify the genders of the 'Non-casual developer' 2 . We applied SentiSE, a customized and state-of-the-art sentiment analysis tool for the SE domain to identify the sentiment polarity of each code review comment.…”

Section: Introductionmentioning

confidence: 99%

Expressions of Sentiments during Code Reviews: Male vs. Female

Paul

Bosu

Sultana

2019

2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Background: As most of the software development organizations are male-dominated, female developers encountering various negative workplace experiences reported feeling like they "do not belong". Exposures to discriminatory expletives or negative critiques from their male colleagues may further exacerbate those feelings.Aims: The primary goal of this study is to identify the differences in expressions of sentiments between male and female developers during various software engineering tasks.Method: On this goal, we mined the code review repositories of six popular open source projects. We used a semi-automated approach leveraging the name as well as multiple social networks to identify the gender of a developer. Using SentiSE, a customized and state-of-the-art sentiment analysis tool for the software engineering domain, we classify each communication as negative, positive, or neutral. We also compute the frequencies of sentiment words, emoticons, and expletives used by each developer.Results: Our results suggest that the likelihood of using sentiment words, emoticons, and expletives during code reviews varies based on the gender of a developer, as females are significantly less likely to express sentiments than males. Although female developers were more neutral to their male colleagues than to another female, male developers from three out of the six projects were not only writing more frequent negative comments but also withholding positive encouragements from their female counterparts.Conclusion: Our results provide empirical evidence of another factor behind the negative work place experiences encountered by the female developers that may be contributing to the diminishing number of females in the SE industry.

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“…Indeed, research indicates subjective evaluations of fit often lead individuals to conclude that women are not well positioned to fill roles in historically male-dominated fields (Boring, Ottoboni, and Stark 2016;Cassese and Holman 2017;Foschi 2000;Moss-Racusin et al 2012). By contrast, when evaluations focus on objective criteria, the contributions and qualifications of women are more likely to be recognized and valued (Goldin and Rouse 2000;Terrell et al 2017).…”

mentioning

confidence: 99%