The rapid adoption of generative AI in software development has impacted the industry, yet its effects 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 beneficial and reported significant advantages, they also highlighted accessibility challenges. Specifically, 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 difficult 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 findings emphasize the need to apply activity-centered design principles to generative AI assistants, ensuring they better align with user behaviors and address specific accessibility needs. This approach can enable the assistants to provide more intuitive, inclusive, and effective experiences, while also contributing to the broader goal of enhancing accessibility in software development.

The usability of APIs is increasingly important to programmer productivity. Based on experience with usability studies of specific APIs, techniques were explored for studying the usability of design choices common to many APIs. A comparative study was performed to assess how professional programmers use APIs with required parameters in objects' constructors as opposed to parameterless "default" constructors. It was hypothesized that required parameters would create more usable and self- documenting APIs by guiding programmers toward the correct use of objects and preventing errors. However, in the study, it was found that, contrary to expectations, programmers strongly preferred and were more effective with APIs that did not require constructor parameters. Participants' behavior was analyzed using the cognitive dimensions framework, and revealing that required constructor parameters interfere with common learning strategies, causing undesirable premature commitment.

Despite years of availability of testing tools, professional software developers still seem to need better support to determine the effectiveness of their tests. Without improvements in this area, inadequate testing of software seems likely to remain a major problem. To address this problem, industry and researchers have proposed systems that visualize "testedness" for end-user and professional developers. Empirical studies of such systems for end-user programmers have begun to show success at helping end users write more effective tests. Encouraged by this research, we examined the effect that code coverage visualizations have on the effectiveness of test cases that professional software developers write. This paper presents the results of an empirical study conducted using code coverage visualizations found in a commercially available programming environment. Our results reveal how this kind of code coverage visualization impacts test effectiveness, and provide insights into the strategies developers use to test code.