CASE STUDY

33% More Delivery, Same QA Team: An AI-augmented Software Testing

  • 33% Higher Ticket Throughput, Zero QA Headcount Increase
  • 28–38% Productivity Boost in Low and Medium Complexity Work

Client Profile

The client is an engineering group that provides agile sprints with iterative updates to the platform. As the majority of the workload focused on low- to medium-complexity tickets and fixed team capacity, they have used AI tools to increase productivity and throughput of delivery.

Industry Focus Software and Technology Industry Focus Software and Technology

Industry Focus
Software and Technology

Challenge

The introduction of AI-assisted development practices led to an improvement of the productivity of the team working on low- and medium-complexity tasks by 28-38%. The total throughput of tickets increased by ~33% without increasing the headcount.

This acceleration exposed several QA challenges:

  • Without additional QA capacity, validation work per sprint went up.
  • Fewer delivery cycles minimized the time used to do a comprehensive regression.
  • QA processes that were manual and heavy became a bottleneck.
  • Test cycle predictability reduced with throughput.

Without adapting QA workflows, the risk of escaped defects and inconsistent validation outcomes increased.

QASource Solution

QASource adopted an AI-augmented QA system that aimed at throughput-oriented validation and predictive quality results. The major elements of the solution were:

  • Sprint scope and change impact assessment with AI.
  • Introduction of test coverage expansion in accordance with AI-based delivery streams.
  • Faster automation to approve higher volumes of tickets.
  • Risk-informed regression selection to achieve efficiency.
  • Structured reporting to maintain visibility in quicker cycles.

The solution guaranteed the QA capacity to increase with the delivery velocity and not be a bottleneck. 

Execution Highlights

Risk Interpretation at the Sprint Level

Analyzed delivery patterns and ticket complexity.

Focus: QA prioritized high-risk tickets to reduce release risk.

Accelerated Validation Coverage

Quick test designing and larger throughput.

Approach: Reduced manual-only validation.

Regression Optimization

Localized regression of affected regions.

Outcome: More predictable tests with greater release frequency.

Outcome

 As the delivery throughput more or less increased by 33%, QA had to expand validation without new personnel. QASource introduced an AI-enhanced QA system that enhanced the test coverage, velocity of validation, and regression to facilitate faster sprint cycles.