"@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions

2021 ◽  
Vol 5 (CSCW2) ◽  
pp. 1-25
Author(s):  
Ashish Chopra ◽  
Morgan Mo ◽  
Samuel Dodson ◽  
Ivan Beschastnikh ◽  
Sidney S. Fels ◽  
...  
Keyword(s):  
Pull Request

scholarly journals Action-based Recommendation in Pull-request Development

2020 ◽  
Author(s):  
Muhammad Ilyas Azeem ◽  
Sebastiano Panichella ◽  
Andrea Di Sorbo ◽  
Alexander Serebrenik ◽  
Qing Wang
Keyword(s):  
Pull Request

scholarly journals Topic-Based Integrator Matching for Pull Request

2017 ◽  
Author(s):  
Zhifang Liao ◽  
Yanbing Li ◽  
Dayu He ◽  
Jinsong Wu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Pull Request

Creating a “Pull Request” for Collaborative Care

2013 ◽  
Vol 64 (9) ◽  
pp. 825-825
Author(s):  
Andres Barkil-Oteo
Keyword(s):  
Collaborative Care ◽  
Pull Request

scholarly journals Core-reviewer recommendation based on Pull Request topic model and collaborator social network

2019 ◽  
Vol 24 (8) ◽  
pp. 5683-5693 ◽  
Author(s):  
Zhifang Liao ◽  
ZeXuan Wu ◽  
Yanbing Li ◽  
Yan Zhang ◽  
Xiaoping Fan ◽  
...  
Keyword(s):  
Social Network ◽  
Topic Model ◽  
Pull Request

scholarly journals Who should make decision on this pull request? Analyzing time-decaying relationships and file similarities for integrator prediction

2019 ◽  
Vol 154 ◽  
pp. 196-210
Author(s):  
Jing Jiang ◽  
David Lo ◽  
Jiateng Zheng ◽  
Xin Xia ◽  
Yun Yang ◽  
...  
Keyword(s):  
Pull Request

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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