Master Thesis Presentation - Daria Ana-Maria Mogoș

Title: Generative AI and the Productivity of Developers with Disabilities

Abstract:
Generative AI tools have become increasingly integrated into software development workflows and are frequently associated with measurable improvements in developer productivity. However, while existing research has primarily focused on the general developer population, limited attention has been given to how the effects of AI-assisted development tools extend to developers with disabilities. This study investigates the relationship between Generative AI adoption and developer productivity among GitHub users who self-identify as having a disability. From an initial pool of over 3000 users, we constructed a dataset of 199 GitHub users across multiple disability categories, using publicly available profile information. These users were selected based on predefined activity thresholds and adoption of Generative AI tools was inferred from direct references to AI systems in commit messages. Their productivity was evaluated within and across disability groups through metrics such as commits, code contributions and pull request acceptance rates. Through these, we found limited evidence of measurable productivity changes following adoption. While most disability groups did not exhibit statistically significant differences across the chosen metrics, the broader disabled group showed a statistically significant increase in net lines modified after adoption. This suggests that the relationship between Generative AI adoption and developer productivity may be more complex than expected, and that GitHub metrics alone may not adequately capture developers' experiences. To provide additional context for interpreting these quantitative findings, a complementary survey was designed to explore the developers' perception of productivity, activity levels and accessibility-related experiences with Generative AI tools.

Supervisors: Ayushi Rastogi, Paris Avgeriou