SHF: Small: Retrospective and Prospective Studies of the Effects of Gender Bias in Software Engineering

性别偏见在社会科学界得到了广泛的研究，但在软件工程领域的研究相对较少，尽管这些问题在技术领域变得突出。2017年，研究者发表了一项关于大规模开源中的性别差异和偏见的研究，研究了开源软件项目的贡献成功率。结果显示，女性的贡献被接受的可能性比男性高，但当拉请求者的性别可见时，趋势就相反了。通过增加对减少女性排他性的偏见的理解，这个项目旨在扩大参与范围，并为社会带来重大利益。女性是软件工程中最大的代表性不足的群体。该项目将利用一个独特而广泛的数据集，对软件工程中的性别偏见进行四个系列的基础研究。第一批研究将使用几种新方法重新检查现有的研究。第二个家庭将使用其他平台和其他结果测量对先前的结果进行三角测量。第三个家庭将调查先前文献中发现的偏见的四个方面。第四类研究将是分离偏见影响的实验。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Gender bias has been researched extensively in the social science community, but relatively little research has been done in software engineering specifically, despite such issues coming to prominence in the technology sphere. In 2017, the investigator published a study on gender differences and bias in open source on a large scale, looking at contribution success rates in open-source software projects. The results showed that the likelihood of a contribution being accepted was higher for women than for men, but the trend is reversed when the gender of the pull requester is visible. By increasing understanding of the biases that decrease exclusivity of women, the largest underrepresented group in software engineering, this project aims to broaden participation and have significant benefits to society.The project will conduct four families of foundational studies about gender bias in software engineering, leveraging a unique and extensive dataset. The first family of studies will re-examine existing studies using several new methods. The second family will triangulate prior results using other platforms and other outcome measures. The third family will investigate four aspects of bias uncovered in prior literature. The fourth family of studies will be experiments that isolate the effect of biases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.