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AFRISS

How TestWheel Enabled Repeatable Automated Regression in a Secure Federal Recruiting System

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AFRISS is a secure federal recruiting operations system supporting applicant tracking, workflow management, and reporting across distributed recruiting units. The platform manages mission-critical recruiting processes that require high reliability, workflow accuracy, and secure access controls.

Because AFRISS operates in an active federal environment with frequent updates and downstream process dependencies, every system change must be thoroughly validated. Even small modifications carry risk of unintended side effects across interconnected workflows.

To reduce manual testing overhead and strengthen regression reliability, the AFRISS team introduced TestWheel to build sustainable automation coverage within its secure CAC-enabled environment

Challenges Before TestWheel

Lengthy, manual regression cycles

Fully Manual Functional Testing

Prior to TestWheel, AFRISS relied entirely on manual functional validation. Testing was performed case-by-case, primarily focused on newly introduced changes. Previously executed workflows were rerun when time allowed, but full regression coverage was not consistently feasible.

Limited coverage due to effort constraints

Time-Intensive Repetitive Workflows

Dozens of workflows had to be manually executed for comprehensive regression validation. This required significant tester effort and made full regression impractical for every release.

Tester bottleneck & resource risk

Limited Regression Coverage

Testing efforts were concentrated on the immediate scope of changes. Broader system validation was often constrained by time and staffing, increasing the risk of undetected side effects.

Maintenance overhead with evolving application

Inconsistent Validation of Downstream Impacts

Because regression was manual and selective, unintended impacts to unrelated modules could go undetected until later in the release cycle.

Lack of formal defect leakage tracking & reporting

Heavy Dependence on Manual Resources

Testing scalability was directly tied to available personnel. Increasing coverage required increased manual effort, making expansion inefficient.

Goals & Requirements

To modernize its testing approach, AFRISS required a solution that would:

  • Reduce repetitive manual testing effort
  • Enable consistent full regression coverage
  • Improve testing efficiency without increasing headcount
  • Allow manual testers to build automation without advanced programming skills
  • Operate effectively within a secure federal environment requiring CAC authentication
  • Maintain stability across dynamic web application behavior

Solution: Implementing TestWheel at AFRISS

TestWheel was introduced alongside ongoing development to build functional and regression coverage incrementally.

Initial Automation Deployment

Within the first month:

  • Approximately 12 automated functional tests were created and executing successfully
  • A foundational regression suite was established
  • Core system workflows began transitioning to repeatable automated validation

As adoption continued, an AFRISS tester developed a comprehensive regression suite consisting of dozens of automated test cases and hundreds of individual steps covering mission-critical workflows.

Addressing Dynamic Application Behavior

During early implementation, automation stability was impacted by dynamic selectors within the AFRISS application.

The team refined their automation strategy using:

  • Stable static attributes where available
  • Relative XPath strategies
  • CSS selectors
  • Full XPath selectors when necessary

Once selector strategies were aligned with application behavior, test execution stability improved significantly.

Secure CAC Environment Compatibility

AFRISS requires CAC-based authentication for system access. Integration with ECA Soft Cert streamlined automated authentication, allowing TestWheel to execute within the secure federal framework without additional manual overhead. This ensured automation could function compliantly inside a controlled federal environment.

Low-Code Test Creation Model

TestWheel’s Excel-based test creation model and web-based editor enabled a manual tester to successfully build and maintain automation coverage without deep development expertise. This reduced adoption friction and demonstrated that automation could scale without expanding engineering resources.

Results & Operational Impact

Metric / Area Before (Manual Testing) After TestWheel Observed Impact
Regression execution Manual execution of dozens of workflows One-click automated regression Significant time savings
Coverage consistency Limited, change-focused validation Repeatable full regression suite Broader system validation
Resource utilization High manual dependency Automation-led with oversight Increased efficiency
Release confidence Moderate High with consistent validation Reduced downstream risk
Documentation visibility Manual reporting Step-level logs + screenshots Improved traceability

Operational Improvements

  • Repeatable regression testing across releases
  • Reduced repetitive manual effort
  • Faster validation cycles
  • Increased confidence in downstream process integrity
  • Improved defect identification through detailed execution logs
  • Greater stakeholder visibility into test results

Why it Worked

Why it Worked

Built for Secure Federal Environments

TestWheel successfully operated within CAC-authenticated infrastructure, validating its compatibility with high-security federal systems.

Why it Worked

Sustainable Automation Adoption

A manual tester was able to design, build, and maintain a robust regression suite, demonstrating that automation scalability did not require expanding development teams.

Why it Worked

Improved Stability Through Refined Strategy

By adapting selector strategies to dynamic application behavior, the team achieved consistent test reliability and long-term maintainability

Why it Worked

Enhanced Reporting & Transparency

Detailed step-level reporting with screenshots and execution logs simplified issue identification and strengthened communication between QA and development teams.

Future Roadmap

Building on its automation foundation, AFRISS plans to:

  • Expand regression coverage across additional workflows
  • Increase automation depth across reporting modules
  • Further optimize selector strategies for maintainability
  • Incorporate automation earlier in development cycles
  • Continue balancing manual exploratory testing with scalable automated validation

By transitioning from fully manual functional testing to repeatable automated regression, AFRISS significantly reduced operational risk while improving release confidence within a secure federal recruiting

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