AI-POWERED QA TESTING ACCELERATION
OVERVIEW
A global enterprise client within the ERP technology space needed to increase QA test coverage and speed without adding sprint capacity. Fiduciary Tech consultants embedded a practical AI-first QA workflow using the client’s approved internal AI tools to generate stronger test cases and accelerate test automation. The result was faster delivery of high-quality tests, reduced manual scripting, and improved maintainability of the automation suite.
THE CHALLENGE
Acceptance criteria in user stories frequently bundled multiple requirements into single statements, forcing QA engineers to manually interpret and split them into atomic scenarios, which were then used to create independent test cases. At the same time, automation work was deprioritized inside sprints in favor of quick manual feature testing, leaving minimal time to build or expand automated coverage. Manual test design and execution slowed delivery and increased the risk of missing regression defects or edge cases, as well as producing inconsistent coverage across releases.
OUR SOLUTION
We integrated AI into both test design and automation development. First, we used the client’s internal context-aware AI agent to generate positive and negative test cases directly from acceptance criteria. Because the criteria were often complex or overly combined, QA engineers streamlined and restructured them into clearer inputs before prompting the AI to break outputs into atomic, one-feature-per-test cases. Human review remained a required step to confirm coverage and ensure accurate alignment with the criteria.
Second, we used the client’s internal AI coding assistant within the development environment to accelerate UI test automation. The assistant generated reliable element locators from inspected UI components, detected repetitive patterns, proposed reusable methods, helped debug scripts interactively, and suggested optimizations to improve execution speed and code quality.
A standout outcome was an automated test-case extraction tool built with AI support. The tool scans the test repository and produces an instant, refreshable list of automation coverage per module.
KEY FEATURES
- • AI-generated test cases from acceptance criteria, including positive and negative scenarios
• Prompt-driven conversion of combined tests into atomic, one-behavior-per-test cases
• AI-assisted UI automation: fast locator generation and reusable method creation
• Interactive debugging support for resolving automation logic issues
• Automated repository scanning to inventory test coverage by module
GLOBAL IMPACT/RESULTS
- • Test-case extraction tool built in ~1 day, versus an estimated full sprint manually (≈80% time reduction)
• UI locator generation succeeded ~80% of the time, significantly reducing manual selector writing
• Faster automation script development even with limited sprint bandwidth
• More complete and accurate test coverage through consistent atomic and positive/negative test design
• Reduced human error on repetitive QA and automation tasks
TECHNOLOGIES & SERVICES
Client internal AI agent — context-aware test case generation
Client internal AI coding assistant (IDE-integrated) — automation acceleration, refactoring, debugging
UI test automation framework — scripted functional testing
Custom repository-scanning scripts — automated test inventory generation
CONCLUSION
By pairing AI-driven test design with AI-accelerated automation, we enabled the QA team to deliver broader coverage faster without expanding sprint scope. This approach improves reliability today and provides a scalable foundation for ongoing quality assurance as product complexity grows.