Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering
It addresses a critical gap in SE research by systematically examining neurodivergent women, an overlooked group, to inform inclusive practices.
This paper tackles the problem of neurodivergent women in software engineering facing unique challenges due to gender bias and neurological differences, resulting in stress, burnout, and attrition, by proposing a hybrid methodological approach to uncover and address these issues.
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.