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Websites are integral to people’s daily lives, with billions in use today. However, due to limited awareness of accessibility and its guidelines, developers often release web apps that are inaccessible to people with disabilities, who make up around 16% of the global population. To ensure a baseline of accessibility, software engineers rely on automated checkers that assess a webpage’s compliance based on predefined rules. Unfortunately, these tools typically cover only a small subset of accessibility guidelines and often overlook violations that require a semantic understanding of the webpage. The advent of generative AI, known for its ability to comprehend textual and visual content, has created new possibilities for detecting accessibility violations. We began by studying the most widely used guideline, WCAG, to determine the testable success criteria that generative AI could address. This led to the development of an automated tool called GenA11y, which extracts elements from a page related to each success criterion and inputs them into an LLM prompted to detect accessibility issues on the web. Evaluations of GenA11y showed its effectiveness, with a precision of 94.5% and a recall of 87.61%. Additionally, when tested on real websites, GenA11y identified an average of eight more types of accessibility violations than the combination of existing tools. 

Background: Software engineering requires both technical skills and creative problem-solving. Blind and lowvision software professionals (BLVSPs) encounter numerous workplace challenges, including inaccessible tools and collaboration hurdles with sighted colleagues. Objective: This study explores the innovative strategies employed by BLVSPs to overcome these accessibility barriers, focusing on their custom solutions and the importance of supportive communities. Methodology: We conducted semi-structured interviews with 30 BLVSPs and used reflexive thematic analysis to identify key themes. Results: Findings reveal that BLVSPs are motivated to develop creative and adaptive solutions, highlighting the vital role of collaborative communities in fostering shared problem-solving. Conclusion: For BLVSPs, creative problem-solving is essential for navigating inaccessible work environments, in contrast to sighted peers, who pursue optimization. This study enhances understanding of how BLVSPs navigate accessibility challenges through innovation. 

Existing commercial and in-house software development tools are often inaccessible to blind and low vision software professionals (BLVSPs), hindering their participation and career growth at work. Building on existing research on Do-It-Yourself (DIY) assistive technologies and customized tools made by programmers, we shed light on the currently unexplored intersection of how DIY tools built and used by BLVSPs support accessible software development. Through semi-structured interviews with 30 BLVSPs, we found that such tools serve many different purposes and are driven by motivations such as desiring to maintain a professional image and a sense of dignity at work. These tools had significant impacts on workplace accessibility and revealed a need for a more centralized community for sharing tools, tips, and tricks. Based on our findings, we introduce the “Double Hacker Dilemma” and highlight a need for developing more effective peer and organizational platforms that support DIY tool sharing. 

Despite the availability of numerous automatic accessibility testing solutions, web accessibility issues persist on many websites. Moreover, there is a lack of systematic evaluations of the efficacy of current accessibility testing tools. To address this gap, we present the first mutation analysis framework, called Ma11y, designed to assess web accessibility testing tools. Ma11y includes 25 mutation operators that intentionally violate various accessibility principles and an automated oracle to determine whether a mutant is detected by a testing tool. Evaluation on real-world websites demonstrates the practical applicability of the mutation operators and the framework’s capacity to assess tool performance. Our results demonstrate that the current tools cannot identify nearly 50% of the accessibility bugs injected by our framework, thus underscoring the need for the development of more effective accessibility testing tools. Finally, the framework’s accuracy and performance attest to its potential for seamless and automated application in practical settings 

Scholars have investigated numerous barriers to accessible software development tools and processes for Blind and Low Vision (BLV) developers. However, the research community has yet to study the accessibility of software development meetings, which are known to play a crucial role in software development practice. We conducted semi-structured interviews with 26 BLV software professionals about software development meeting accessibility. We found four key themes related to in-person and remote software development meetings: (1) participants observed that certain meeting activities and software tools used in meetings were inaccessible, (2) participants performed additional labor in order to make meetings accessible, (3) participants avoided disclosing their disability during meetings due to fear of career repercussions, (4) participants suggested technical, social and organizational solutions for accessible meetings, including developing their own solutions. We suggest recommendations and design implications for future accessible software development meetings including technical and policy-driven solutions.