scholarly journals Project level effects of gender on contribution evaluation on GitHub

2017 ◽  
Author(s):  
Pascal Brokmeier
Keyword(s):  
Software Development ◽  
Open Source Software ◽  
Data Science ◽  
Large Datasets ◽  
Male And Female ◽  
Sexual Discrimination ◽  
Pull Request ◽  
Discrimination Factors ◽  
Acceptance Rates ◽  
Project Level

Distributed open source software development has largely turned to GitHub, a pull-based software development collaboration platform. Recent studies have deployed data science techniques on the large datasets available about millions of projects on GitHub. Some research has focused on pull request (PR) acceptance predictors and evidence was found of sexual discrimination among members. In this paper I analyzed the influence of gender on PR acceptance on a project level, comparing different popular projects regarding their discrimination factors. Several projects were identified that have significant differences between male and female PR acceptance rates.

scholarly journals Project level effects of gender on contribution evaluation on GitHub

2017 ◽  
Author(s):  
Pascal Brokmeier
Keyword(s):  
Software Development ◽  
Open Source Software ◽  
Data Science ◽  
Large Datasets ◽  
Male And Female ◽  
Sexual Discrimination ◽  
Pull Request ◽  
Discrimination Factors ◽  
Acceptance Rates ◽  
Project Level

Distributed open source software development has largely turned to GitHub, a pull-based software development collaboration platform. Recent studies have deployed data science techniques on the large datasets available about millions of projects on GitHub. Some research has focused on pull request (PR) acceptance predictors and evidence was found of sexual discrimination among members. In this paper I analyzed the influence of gender on PR acceptance on a project level, comparing different popular projects regarding their discrimination factors. Several projects were identified that have significant differences between male and female PR acceptance rates.

scholarly journals Correction to: Gendered behavior as a disadvantage in open source software development

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Balazs Vedres ◽  
Orsolya Vasarhelyi
Keyword(s):  
United Kingdom ◽  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Data Science ◽  
Open Source Software Development ◽  
Central European ◽  
University Of Oxford ◽  
European University

Following publication of the original article [1], we have been notified that one more affiliation of the corresponding author is missing. Currently Balasz Vedres affiliation is: 1 Oxford Internet Institute, University of Oxford, Oxford, United Kingdom It should be: 1 Oxford Internet Institute, University of Oxford, Oxford, United Kingdom; 2 Department of Network and Data Science, Central European University, Budapest, Hungary.

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

2016 ◽  
Author(s):  
Josh Terrell ◽  
Andrew Kofink ◽  
Justin Middleton ◽  
Clarissa Rainear ◽  
Emerson Murphy-Hill ◽  
...  
Keyword(s):  
Gender Differences ◽  
Open Source ◽  
Gender Bias ◽  
Open Source Software ◽  
Women In The Workplace ◽  
Pull Request ◽  
Acceptance Rates

Biases against women in the workplace have been documented in a variety of studies. This paper presents the largest study to date on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women's contributions tend to be accepted more often than men's. However, women's acceptance rates are higher only when they are not identifiable as women. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

scholarly journals Gender bias in open source: Pull request acceptance of women versus men

2016 ◽  
Author(s):  
Josh Terrell ◽  
Andrew Kofink ◽  
Justin Middleton ◽  
Clarissa Rainear ◽  
Emerson Murphy-Hill ◽  
...  
Keyword(s):  
Open Source ◽  
Gender Bias ◽  
Open Source Software ◽  
Women In The Workplace ◽  
Pull Request ◽  
Acceptance Rates

Biases against women in the workplace have been documented in a variety of studies. This paper presents the largest study to date on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women's contributions tend to be accepted more often than men's. However, when a woman's gender is identifiable, they are rejected more often. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

scholarly journals Gender differences and bias in open source: pull request acceptance of women versus men

2017 ◽  
Vol 3 ◽  
pp. e111 ◽  
Author(s):  
Josh Terrell ◽  
Andrew Kofink ◽  
Justin Middleton ◽  
Clarissa Rainear ◽  
Emerson Murphy-Hill ◽  
...  
Keyword(s):  
Gender Differences ◽  
Open Source ◽  
Gender Bias ◽  
Open Source Software ◽  
Large Scale ◽  
Women In The Workplace ◽  
Large Scale Study ◽  
Pull Request ◽  
Acceptance Rates

Biases against women in the workplace have been documented in a variety of studies. This paper presents a large scale study on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women’s contributions tend to be accepted more often than men’s. However, for contributors who are outsiders to a project and their gender is identifiable, men’s acceptance rates are higher. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

scholarly journals New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 920
Author(s):  
Abdulkadir Şeker ◽  
Banu Diri ◽  
Halil Arslan
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Recommendation System ◽  
Similarity Score ◽  
Open Source Software Development ◽  
Wide Range ◽  
Pull Request ◽  
The Individual ◽  
Collaboration Platforms

Software collaboration platforms where millions of developers from diverse locations can contribute to the common open source projects have recently become popular. On these platforms, various information is obtained from developer activities that can then be used as developer metrics to solve a variety of challenges. In this study, we proposed new developer metrics extracted from the issue, commit, and pull request activities of developers on GitHub. We created developer metrics from the individual activities and combined certain activities according to some common traits. To evaluate these metrics, we created an item-based project recommendation system. In order to validate this system, we calculated the similarity score using two methods and assessed top-n hit scores using two different approaches. The results for all scores with these methods indicated that the most successful metrics were binary_issue_related, issue_commented, binary_pr_related, and issue_opened. To verify our results, we compared our metrics with another metric generated from a very similar study and found that most of our metrics gave better scores that metric. In conclusion, the issue feature is more crucial for GitHub compared with other features. Moreover, commenting activity in projects can be equally as valuable as code contributions. The most of binary metrics that were generated, regardless of the number of activities, also showed remarkable results. In this context, we presented improvable and noteworthy developer metrics that can be used for a wide range of open-source software development challenges, such as user characterization, project recommendation, and code review assignment.

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

2016 ◽  
Author(s):  
Josh Terrell ◽  
Andrew Kofink ◽  
Justin Middleton ◽  
Clarissa Rainear ◽  
Emerson Murphy-Hill ◽  
...  
Keyword(s):  
Gender Differences ◽  
Open Source ◽  
Gender Bias ◽  
Open Source Software ◽  
Women In The Workplace ◽  
Pull Request ◽  
Acceptance Rates

Biases against women in the workplace have been documented in a variety of studies. This paper presents the largest study to date on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women's contributions tend to be accepted more often than men's. However, women's acceptance rates are higher only when they are not identifiable as women. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

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