AXNav: Enhancing Accessibility Testing with Natural Language Instructions

Core Concepts

The author explores using Large Language Models to automate accessibility testing, addressing challenges faced by manual testers.

Abstract

Developers and QA testers face challenges in manual accessibility testing due to the overwhelming scope of features. AXNav uses LLMs to interpret natural language test instructions, execute tests on a cloud device, and flag potential accessibility issues. Key features include VoiceOver navigation, Dynamic Type resizing checks, Button Shapes validation, and video output with chapter markers.

Stats

"A formative study with 6 professional QA and accessibility testers" "10-participant user study with accessibility QA professionals" "85.5% overall accuracy for regression testing dataset" "70.0% overall accuracy for free apps dataset"

Quotes

Key Insights Distilled From

AXNav

by Maryam Taeb,... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2310.02424.pdf

Deeper Inquiries

How can the use of LLMs in controlling assistive technologies impact future accessibility testing workflows?

The use of Large Language Models (LLMs) in controlling assistive technologies can significantly impact future accessibility testing workflows by automating and streamlining the process. LLMs can interpret natural language test instructions, formulate actionable steps, and execute them on a device, reducing the manual effort required for accessibility testing. This automation can lead to increased efficiency, faster turnaround times for testing, and improved coverage of accessibility features across different apps. By leveraging LLMs, testers can rely on automated systems to navigate through apps using assistive features like VoiceOver or Dynamic Type accurately based on natural language instructions.

What are the limitations of automated tools compared to manual testing in detecting accessibility issues?

While automated tools offer many advantages in terms of speed and scalability, they also have limitations when compared to manual testing in detecting certain types of accessibility issues. One limitation is that automated tools may not always capture the user experience accurately as a human tester would. They might miss subtle nuances or context-specific issues that require human judgment and intuition to identify effectively. Automated tools may also struggle with dynamic content or complex interactions that are challenging to simulate programmatically. Additionally, automated tools may produce false positives or false negatives when identifying accessibility issues due to their reliance on predefined rules or algorithms. Human testers bring contextual understanding and domain expertise that enable them to discern between genuine problems and technical glitches more effectively than automated systems.

How can natural language-based automation improve efficiency in other areas beyond accessibility testing?

Natural language-based automation has the potential to enhance efficiency across various domains beyond just accessibility testing. In software development, natural language processing (NLP) techniques can be leveraged for requirements gathering, code generation from plain text descriptions, documentation creation, and even bug tracking. For project management tasks, NLP-powered automation could streamline communication processes by automatically summarizing meeting notes or generating reports from conversational data. In customer service operations, chatbots powered by NLP technology could provide personalized assistance based on user queries without requiring human intervention constantly. Overall, natural language-based automation has broad applications across industries such as healthcare (patient data analysis), finance (automated report generation), marketing (content creation), etc., offering opportunities for increased productivity and streamlined workflows through intelligent interpretation of human language inputs into actionable tasks.