Building AI-Powered Testing Solutions with AWS Bedrock

Exploring how large language models can transform accessibility testing and defect identification in modern web applications.

AI AWS Bedrock Testing Automation Accessibility

Introduction

The intersection of artificial intelligence and software testing is creating unprecedented opportunities for quality assurance teams. In this post, I’ll share insights from building Argus, an AI accessibility assistant that leverages AWS Bedrock for intelligent defect detection.

The Challenge

Traditional accessibility testing relies heavily on rule-based scanners that can identify obvious WCAG violations but often miss nuanced issues that affect real users. These tools generate numerous false positives while missing contextual problems that require human judgment.

Enter Large Language Models

AWS Bedrock provides access to foundation models that can understand context, interpret visual layouts, and reason about user experience in ways that rule-based systems cannot. By combining custom prompt engineering with multi-platform testing strategies, we built a system that:

  • Analyzes page structure and content semantically
  • Identifies accessibility issues that require contextual understanding
  • Provides actionable remediation guidance
  • Reduces false positives through intelligent filtering

Key Learnings

1. Prompt Engineering Matters

The quality of AI-assisted testing depends heavily on how you frame the problem. We developed specialized prompts for different accessibility concerns:

Analyze this component for keyboard navigation issues.
Consider the tab order, focus indicators, and whether
all interactive elements are reachable without a mouse.

2. Combine AI with Traditional Tools

AI doesn’t replace existing accessibility scanners—it augments them. We use rule-based tools for definitive violations and AI for nuanced analysis.

3. Human-in-the-Loop

AI suggestions require human validation. Our workflow presents findings to QA engineers who confirm or dismiss issues before they enter the bug tracking system.

Results

By integrating AI-powered analysis into our accessibility testing workflow, we achieved:

  • 40% reduction in false positives
  • Identification of contextual issues previously missed
  • Faster triage through intelligent categorization

Conclusion

AI-powered testing is not about replacing human testers—it’s about augmenting their capabilities. Tools like AWS Bedrock make it practical to bring LLM capabilities into the testing pipeline, improving both coverage and accuracy.

Stay tuned for more posts on building effective test automation at scale.