A Multifaceted View on Discrimination in Software Development Careers
October 25, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Shalini Chakraborty, Sebastian Baltes
arXiv ID
2510.22457
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Conversations around diversity and inclusion in software engineering often focus on gender and racial disparities. However, the State of Devs 2025 survey with 8,717 participants revealed that other forms of discrimination are similarly prevalent but receive considerably less attention. This includes discrimination based on age, political perspective, disabilities, or cognitive differences such as neurodivergence. We conducted a secondary analysis of 800 open-ended survey responses to examine patterns of perceived discrimination, as well as related challenges and negative impacts. Our study covers multiple identity facets, including age, gender, race, and disability. We found that age- and gender-related discrimination was the most frequently reported workplace issue, but discrimination based on political and religious views emerged as further notable concerns. Most of the participants who identified as female cited gender as the primary source of discrimination, often accompanied by intersectional factors such as race, political views, age, or sexual orientation. Discrimination related to caregiving responsibilities was reported by all gender identities. Regarding the negative impacts of workplace issues, many participants described modifying their appearance or behavior in response to gender biases. Gender also appeared to influence broader career challenges, as women and non-binary respondents reported experiencing almost all workplace issues at higher rates, particularly discrimination (35%) and mental health challenges (62%). Our goal is to raise awareness in the research community that discrimination in software development is multifaceted, and to encourage researchers to select and assess relevant facets beyond age and gender when designing software engineering studies.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted