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Investigating the Experience of Autistic Individuals in Software Engineering

Madalena Sasportes, Grischa Liebel, Miguel Goulão

TL;DR

This paper investigates the strengths and challenges of autistic software engineers in SE activities using a mixed-methods STGT approach (16 interviews and a 49-participant survey). It compares findings with the theory on neurodivergent cognitive dysfunction in SE proposed by Gama et al., and extends it by highlighting strengths (e.g., problem-solving, efficiency, flow) and showing that challenges can extend into code and abstractions, with team composition as a potential moderator. Key contributions include eight SE-relevant categories, partial support for existing theory, and proposed avenues for enabling inclusive practice, such as tool support for code reviews and adapted agile processes. The work emphasizes the practical value of neurodiverse strengths in SE and calls for larger-scale quantitative validation and targeted tool and process improvements to foster inclusivity.

Abstract

Context: Autism spectrum disorder (ASD) leads to various issues in the everyday life of autistic individuals, often resulting in unemployment and mental health problems. To improve the inclusion of autistic adults, existing studies have highlighted the strengths these individuals possess in comparison to non-autistic individuals, e.g., high attention to detail or excellent logical reasoning skills. If fostered, these strengths could be valuable in software engineering activities, such for identifying specific kinds of bugs in code. However, existing work in SE has primarily studied the challenges of autistic individuals and possible accommodations, with little attention their strengths. Objective: Our goal is to analyse the experiences of autistic individuals in software engineering activities, such as code reviews, with a particular emphasis on strengths. Methods: This study combines Social-Technical Grounded Theory through semi-structured interviews with 16 autistic software engineers and a survey with 49 respondents, including 5 autistic participants. We compare the emerging themes with the theory by Gama et al. on the Effect of Neurodivergent Cognitive Dysfunctions in Software Engineering Performance. Results: Our results suggest that autistic software engineers are often skilled in logical thinking, attention to detail, and hyperfocus in programming; and they enjoy learning new programming languages and programming-related technologies. Confirming previous work, they tend to prefer written communication and remote work. Finally, we report a high comfort level in interacting with AI-based systems. Conclusions: Our findings extend existing work by providing further evidence on the strengths of autistic software engineers.

Investigating the Experience of Autistic Individuals in Software Engineering

TL;DR

This paper investigates the strengths and challenges of autistic software engineers in SE activities using a mixed-methods STGT approach (16 interviews and a 49-participant survey). It compares findings with the theory on neurodivergent cognitive dysfunction in SE proposed by Gama et al., and extends it by highlighting strengths (e.g., problem-solving, efficiency, flow) and showing that challenges can extend into code and abstractions, with team composition as a potential moderator. Key contributions include eight SE-relevant categories, partial support for existing theory, and proposed avenues for enabling inclusive practice, such as tool support for code reviews and adapted agile processes. The work emphasizes the practical value of neurodiverse strengths in SE and calls for larger-scale quantitative validation and targeted tool and process improvements to foster inclusivity.

Abstract

Context: Autism spectrum disorder (ASD) leads to various issues in the everyday life of autistic individuals, often resulting in unemployment and mental health problems. To improve the inclusion of autistic adults, existing studies have highlighted the strengths these individuals possess in comparison to non-autistic individuals, e.g., high attention to detail or excellent logical reasoning skills. If fostered, these strengths could be valuable in software engineering activities, such for identifying specific kinds of bugs in code. However, existing work in SE has primarily studied the challenges of autistic individuals and possible accommodations, with little attention their strengths. Objective: Our goal is to analyse the experiences of autistic individuals in software engineering activities, such as code reviews, with a particular emphasis on strengths. Methods: This study combines Social-Technical Grounded Theory through semi-structured interviews with 16 autistic software engineers and a survey with 49 respondents, including 5 autistic participants. We compare the emerging themes with the theory by Gama et al. on the Effect of Neurodivergent Cognitive Dysfunctions in Software Engineering Performance. Results: Our results suggest that autistic software engineers are often skilled in logical thinking, attention to detail, and hyperfocus in programming; and they enjoy learning new programming languages and programming-related technologies. Confirming previous work, they tend to prefer written communication and remote work. Finally, we report a high comfort level in interacting with AI-based systems. Conclusions: Our findings extend existing work by providing further evidence on the strengths of autistic software engineers.

Paper Structure

This paper contains 40 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Neurodiversity
  4. Autism Spectrum Disorder (ASD)
  5. Neurodiversity in software engineering
  6. Challenges
  7. Strengths
  8. Method
  9. Socio-Technical Grounded Theory
  10. Sampling and recruitment
  11. Data collection
  12. Data analysis
  13. Validation survey
  14. Validity threats and limitations
  15. Construct validity
  16. ...and 25 more sections

Figures (3)

  • Figure 1: Theory on the Effect of Neurodivergent Cognitive Dysfunctions in Software Engineering Performance Gama2025ICSE-SEIS. Reproduced with Authors' Permission.
  • Figure 2: Categories Overview. Green rectangles indicate a strength, red rectangles a challenge, and yellow rectangles a mixed strength/challenge category. Due to the amount of evidence, "Communication through code" and "Adapting to change" have sub-categories. Finally, bold-faced categories with a star (*) symbol indicate that at least some of the related questions in the follow-up survey showed a significant difference between autistic and non-autistic participants.
  • Figure 3: Updated Theory on the Effect of Neurodivergent Cognitive Dysfunctions in Software Engineering Performance. Additions are depicted in violet colour.