Case Study

AI-Assisted Test Definition & Automation

AI-assisted testing reduced manual effort, strengthened traceability, and improved visibility into test coverage.

RESULTS

Greater Coverage Visibility

Reduced Manual Test Preparation

Stronger End-to-End Traceability

OVERVIEW

Project

Enterprise modernization program

Industry

California State Government

Client

Large state department

The Challenge

The project needed broader test coverage without adding time or resource burden.

As new teams joined an active modernization program, they needed to quickly understand business requirements, solution behavior, existing test coverage, and system dependencies.

Test information existed across requirements, documentation, development artifacts, defects, and multiple workstreams. Building and maintaining test coverage manually required significant time and domain knowledge at a point when the project could not afford to slow down.

Our Solution

AI-assisted testing accelerated scenario development, coverage analysis, test-data preparation, and execution review while keeping human validation in the loop.

The AI Lab embedded directly with testing, development, business, and defect-management teams to help turn fragmented project knowledge into usable test assets.

The team used AI-assisted techniques to:

Analyze requirements and business workflows.
Identify potential test scenarios and coverage gaps.
Support test definition and reusable automation.
Improve requirements-to-test traceability.
Assist SIT and UAT teams with test-data preparation.
Analyze execution results and surface relevant failures.

Human review remained part of critical decisions. AI accelerated analysis and content generation, while project specialists validated the resulting scenarios and automation before implementation.

Results

Improved test efficiency and visibility across thousands of tracked project data points

The solution reduced the amount of time teams spent searching for context and manually translating project information into tests.

Testing became more connected to requirements, defects, development, and business processes, helping reduce silos between workstreams. The client gained stronger visibility into whether features had been fully implemented and tested, including across thousands of tracked project data points.

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