She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models

R. Prikladnicki Karina Kohl

5 Oktober 2022

There is a known and established gender imbalance in software engineering structures. The discussions about gender diversity in Software Engineering are on the table; however, which are the benefits and the difficulties people in software development teams see in gender diversity? For this work, we conducted a survey to qualitatively understand the perceived benefits and difficulties of gender diversity in software development teams. We found out that gender-diverse workplaces are prone to have better ideas sharing, better decision making, creativity, and innovation. Respondents mentioned that some companies worked to improve the hiring process to be more gender-inclusive. Women’s support and inspiration were shared, and some men reported being touched by the subject and diligently are deconstructing their prejudice and misconceptions about women in technology. There are also difficulties. It is common to see only one woman in teams or just a few. More than that, no other gender than men and women, so the white, cisgender man is the pattern most of the time. The same pattern repeats itself in leadership positions leading to male protectionism and privileges. Additionally, other dimensions of diversity pervaded the answers, like intersectionality, race/ethnicity, ageism, and a less explored point: social vulnerability.

Hana Frluckaj J. Herbsleb Laura A. Dabbish + 2 lainnya

7 November 2022

Open source software represents an important form of digital infrastructure as well as a pathway to technical careers for many developers, but women are drastically underrepresented in this setting. Although there is a good body of literature on open source participation, there is very little understanding of the participation trajectories and contribution experiences of women developers, and how they compare to those of men developers, in open source software projects. In order to understand their joining and participation trajectories, we conducted interviews with 23 developers (11 men and 12 women) who became core in an open source project. We identify differences in women and men's motivations for initial contributions and joining processes (e.g. women participating in projects that they have been invited to) and sustained involvement in a project. We also describe unique negative experiences faced by women contributors in this setting in each stage of participation. Our results have implications for diversifying participation in open source software and understanding open source as a pathway to technical careers.

Alicia E. Boyd Hana Winchester Brittany Johnson

1 Mei 2022

The growing ubiquity of machine learning technologies has led to concern and concentrated efforts at improving data-centric research and practice. While much work has been done on addressing equity concerns with respect to unary identities (e.g, race or gender), little to no work in Software Engineering has studied intersectionality to determine how we can provide equitable outcomes for complex, overlapping social identities in data-driven tech. To this end, we designed a survey to learn the landscape of intersectional identities in tech, where these populations contribute data, and how marginalized populations feel about the impact technology has on their day to day lives. Our data thus far, collected from 12 respondents and composed mostly of white and male identities, further highlights the lack of representation in modern data sets and need for contributions that explicitly explore how to support data-driven research and development. ACM Reference Format: Hana Winchester, Alicia E. Boyd, and Brittany Johnson. 2022. An Exploration of Intersectionality in Software Development and Use. In Third Workshop on Gender Equality, Diversity, and Inclusion in Software Engineering (GE@ICSE’22), May 20, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3524501.3527605

Igor Steinmacher M. Gerosa Anita Sarma + 1 lainnya

18 November 2023

Research within sociotechnical domains, such as software engineering, fundamentally requires the human perspective. Nevertheless, traditional qualitative data collection methods suffer from difficulties in participant recruitment, scaling, and labor intensity. This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI), especially large language models (LLMs) like ChatGPT and multimodal foundation models. We explore the potential of AI-generated synthetic text as an alternative source of qualitative data, discussing how LLMs can replicate human responses and behaviors in research settings. We discuss AI applications in emulating humans in interviews, focus groups, surveys, observational studies, and user evaluations. We discuss open problems and research opportunities to implement this vision. In the future, an integrated approach where both AI and human-generated data coexist will likely yield the most effective outcomes.

Lucia Happe Barbora Buhnova

1 Juli 2022

We share findings from a study of 139 women, revealing the frustrations they felt along their way to software engineering and pinpointing promising solutions, such as interdisciplinary education, which could be of enormous help to retaining women in computing.