LLM-as-a-Judge for Scalable Test Coverage Evaluation: Accuracy, Operational Reliability, and Cost
Revolutionizing QA with AI-Powered Test Coverage Assessment
Authors: Donghao Huang, Shila Chew, Anna Dutkiewicz, Zhaoxia Wang
Publication: arXiv:2512.01232v1 [cs.SE] 1 Dec 2025
Executive Impact: Key Metrics for Enterprise AI Adoption
This study highlights critical advancements in automated test coverage evaluation using LLMs, offering significant gains in efficiency, reliability, and cost-effectiveness for modern QA pipelines.
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Cost Reduction (vs GPT-5 high)
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Optimal Model Accuracy
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High Reliability
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Cost per 1K Evaluations
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow: LAJ Workflow
Jira Ticket Creation
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Gherkin Script Development
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Expert Annotation (Ground Truth)
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LLM-as-a-Judge Evaluation
Problem Formulation: Given a software requirement specification (e.g., a Jira ticket) R and a corresponding Gherkin acceptance test script T, the goal is to automatically assess the test coverage completeness—the degree to which the test script adequately addresses the specified requirements. An LLM-as-a-Judge model M produces an assessment AM(R,T) ∈ [0,100] representing estimated coverage percentage. We evaluate M against expert-provided ground truth A*(R,T) to measure assessment quality.
| Input | Process | Output |
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GPT-40 Mini
Achieves best overall accuracy with 6.07% MAAE, demonstrating superior precision.
| Tier | MAAE Range | Key Models | Characteristics |
|---|---|---|---|
| Elite Performance | < 7.0% |
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| Strong Performance | 7.0-9.0% |
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| Economy Tier | > 9.0% |
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GPT-40 Mini: The Optimal Production Choice
GPT-40 Mini stands out as the optimal production model due to its best-in-class accuracy (6.07 MAAE, 93.93% APS), high reliability (96.6% ECR@1), and exceptional cost-effectiveness ($1.01/1K). This model achieves a remarkable 78× cost reduction compared to GPT-5 (high reasoning) while simultaneously improving accuracy, making it ideal for practical adoption at scale.
Key Results:
- ✓ 78× cost reduction vs. GPT-5 (high)
- ✓ Superior accuracy (6.07 MAAE)
- ✓ High reliability (96.6% ECR@1)
- ✓ Cost-effective at $1.01/1K
| Tier | ECR@1 Range | Key Models | Implications |
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| Perfect Reliability | 100% |
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| High Reliability | > 95% |
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| Moderate Reliability | 90% < ECR@1 < 95% |
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| Low Reliability | < 90% |
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175x
Total cost span across all models, from $0.45/1K to $78.96/1K evaluations.
Reasoning Effort Impact: Reasoning effort significantly impacts both accuracy and cost, with model-family dependent patterns. GPT-5 models generally benefit from higher reasoning with predictable accuracy-cost trade-offs, where reducing reasoning from high to low can yield up to 70% cost savings for a 1.53pp accuracy degradation. In contrast, open-weight models (GPT-OSS) degrade across all dimensions as reasoning effort increases, making higher reasoning levels counterproductive for these models.
Key Recommendations: Practitioners should always measure ECR@1 alongside accuracy to capture operational reliability, implement robust retry logic with a 5-15% overhead budget, and continuously monitor ECR@1, latency, and cost in production. Using adjusted cost metrics for total cost of ownership is crucial. Starting with GPT-40 Mini is recommended for most use cases due to its optimal balance of accuracy, reliability, and cost-effectiveness.
| Limitations | Future Directions |
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Calculate Your Potential ROI
Estimate the efficiency gains and cost savings from implementing LLM-as-a-Judge in your QA workflow.
Your Journey to Scalable AI-Powered QA
A typical implementation timeline for integrating LLM-as-a-Judge into your enterprise QA processes.
Phase 1: Initial Assessment & Framework Setup
Integrate LAJ framework, define core rubrics, and configure initial model evaluation environment. Focus on Gherkin/BDD compatibility.
Phase 2: Pilot Deployment & Metric Baseline
Conduct pilot runs with GPT-40 Mini, establish baseline accuracy (MAAE), reliability (ECR@1), and cost metrics. Implement basic retry logic.
Phase 3: Optimization & Model Selection
Explore model configurations and reasoning efforts. Optimize for accuracy-reliability-cost trade-offs. Refine retry strategies and monitor adjusted cost metrics.
Phase 4: Scalable Integration & Continuous Monitoring
Integrate LAJ into CI/CD pipelines for automated test coverage evaluation. Implement dashboards for real-time monitoring of performance and costs. Iterate based on feedback.
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