Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering
Munazza Zaib, Wei Wang, Dulaji Hidellaarachchi, Isma Farah Siddiqui
TL;DR
ND women in software engineering face compounded barriers from gender bias and neurological differences, including late diagnosis and masking. The paper proposes a three-stage Hybrid InclusiveMag–GenderMag methodology (Scope, Derive, Apply) to map challenges, derive evidence-based personas, and test inclusive analytic practices, supported by a targeted literature review and exploratory surveys. It identifies cognitive, social, organizational, structural, and career-domain challenges and argues that current Agile practices amplify exclusion, while outlining redesigns and future tools. The work aims to shift SE toward intersectional, process-centered inclusion and to produce reusable resources (persona libraries, adapted walkthroughs, digital tools) for teams, educators, and researchers.
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.