“Open source software is changing the technology and workforce landscape. Our work will help open source software tools and technology support diverse cognitive styles that will help bring diversity in thought by enabling diversity in open source contributors.”
– Anita Sarma, associate professor of computer science in the College of Engineering at Oregon State.
- Lead PI: Anita Sarma, associate professor of computer science, Oregon State University
- Co-PI: Margaret Burnett, Distinguished Professor of computer science, Oregon State University
In collaboration with:
- PI: Igor Steinmacher, assistant professor, Northern Arizona University
- Co-PI: Marco Gerosa, associate professor, Northern Arizona University
National Science Foundation
$1.4 million between the two universities, $870,773 to Oregon State.
This research will investigate whether and how open source software tools and technologies have gender biases tied with diverse problem-solving styles, and how to remove any such biases.
This work will harness foundational gender research to provide theory-based yet practical solutions and redesigns of open source software projects to address the underrepresentation of women.
The redesigns and the process of creating inclusive tools will be empirically evaluated to create a compendium of “best practices” for fixing gender-bias bugs, in both products (what suitable fixes are to such bugs) and processes (how open source software teams can work together to fix gender-bias bugs).
Open source is having a significant impact on society, in the products it produces and the career paths that it facilitates. However, women are vastly underrepresented among open source developers. This is a significant concern to these communities because it prevents them from receiving the benefits of a larger talent pool and of team diversity. The problem is perpetuated when women developers miss the learning and professional growth opportunities that open source software projects provide, and are overlooked when open source contributions are used to make hiring decisions. Our work will help break down these gender-bias barriers in tools and technology used in open source software.