Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering

TL;DR

This paper explores the unique challenges faced by neurodivergent women in software engineering, highlighting underdiagnosis, masking, and workplace biases, and proposes a hybrid methodological framework to address these issues.

Contribution

It introduces a novel hybrid approach combining inclusivity and gender-focused frameworks to study neurodivergent women in SE, filling a research gap.

Findings

Identified key cognitive, social, and organizational challenges.

Synthesized challenges related to diagnosis and masking.

Laid groundwork for inclusive analytic methods.

Abstract

Neurodivergent women in Software Engineering (SE) encounter distinctive challenges at the intersection of gender bias and neurological differences. To the best of our knowledge, no prior work in SE research has systematically examined this group, despite increasing recognition of neurodiversity in the workplace. Underdiagnosis, masking, and male-centric workplace cultures continue to exacerbate barriers that contribute to stress, burnout, and attrition. In response, we propose a hybrid methodological approach that integrates InclusiveMag's inclusivity framework with the GenderMag walkthrough process, tailored to the context of neurodivergent women in SE. The overarching design unfolds across three stages, scoping through literature review, deriving personas and analytic processes, and applying the method in collaborative workshops. We present a targeted literature review that synthesize challenges into cognitive, social, organizational, structural and career progression challenges neurodivergent women face in SE, including how under/late diagnosis and masking intensify exclusion. These findings lay the groundwork for subsequent stages that will develop and apply inclusive analytic methods to support actionable change.