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The rapid adoption of generative AI in software development has impacted the industry, yet its efects on developers with visual impairments remain largely unexplored. To address this gap, we used an Activity Theory framework to examine how developers with visual impairments interact with AI coding assistants. For this purpose, we conducted a study where developers who are visually impaired completed a series of programming tasks using a generative AI coding assistant. We uncovered that, while participants found the AI assistant benefcial and reported signifcant advantages, they also highlighted accessibility challenges. Specifcally, the AI coding assistant often exacerbated existing accessibility barriers and introduced new challenges. For example, it overwhelmed users with an excessive number of suggestions, leading developers who are visually impaired to express a desire for “AI timeouts.” Additionally, the generative AI coding assistant made it more difcult for developers to switch contexts between the AI-generated content and their own code. Despite these challenges, participants were optimistic about the potential of AI coding assistants to transform the coding experience for developers with visual impairments. Our fndings emphasize the need to apply activity-centered design principles to generative AI assistants, ensuring they better align with user behaviors and address specifc accessibility needs. This approach can enable the assistants to provide more intuitive, inclusive, and efective experiences, while also contributing to the broader goal of enhancing accessibility in software development
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Artifact to Accompany "33 Years of Mathematicians and Software Engineers: A Case Study of Proof Assistant Ecosystems"
As technical computing software, such as MATLAB and SciPy, has gained popularity, ecosystems of interdependent software solutions and communities have formed around these technologies.The development and maintenance of these technical computing ecosystems requires expertise in both software engineering and the underlying technical domain. The inherently interdisciplinary nature of these ecosystems presents unique challenges and opportunities that shape software development practices.Proof assistants, a type of technical computing software, aid users in the creation of formal proofs. In order to examine the influence of the underlying technical domain --- mathematics --- on the development of proof assistant ecosystems, we mined participant activity data from the code repositories and social channels of three popular proof assistants: Lean, Coq, Isabelle. Despite having a shared technical domain, we found little cross-pollination between contributors to the proof assistants. Additionally, we found that most long-term developers focused solely on technical work and did not participate in official social channels. We also found that proof assistant developers specialized into technical subfields. However, the proportion of specialists varied between ecosystems. We did not find evidence that these specialties contributed to fractures within the ecosystems. We discuss the implications of these results on the long-term health and sustainability of proof assistant ecosystems.This artifact contains the scripts and dataset that support an MSR 2024 article.
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- Award ID(s):
- 2100015
- PAR ID:
- 10496129
- Publisher / Repository:
- figshare
- Date Published:
- Subject(s) / Keyword(s):
- Empirical software engineering
- Format(s):
- Medium: X Size: 93199126 Bytes
- Size(s):
- 93199126 Bytes
- Sponsoring Org:
- National Science Foundation
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