A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection

2025-04-26

AI Summary

Regression test suites incur high execution costs, and conventional test case selection (TCS) techniques—such as greedy and genetic algorithms—suffer from substantial computational overhead and poor scalability. To address this, we propose QAOA-TCS, the first framework to apply the quantum approximate optimization algorithm (QAOA) with gate-model quantum circuits to TCS. Our approach formulates test prioritization and coverage analysis as a combinatorial optimization problem and solves it in an ideal quantum simulation environment. Experimental results demonstrate that QAOA-TCS achieves significantly better optimization quality than classical baselines while matching the efficiency of the quantum-annealing-based SelectQA. This work pioneers a novel NISQ-era solution pathway for regression testing optimization, establishing a foundational bridge between software engineering and quantum computing. It represents a critical step toward practical quantum-enhanced software testing on near-term quantum hardware.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Regression testing is key in verifying that software works correctly after changes. However, running the entire regression test suite can be impractical and expensive, especially for large-scale systems. Test suite optimization methods are highly effective but often become infeasible due to their high computational demands. In previous work, Trovato et al. proposed SelectQA, an approach based on quantum annealing that outperforms the traditional state-of-the-art methods, i.e., Additional Greedy and DIV-GA, in efficiency. This work envisions the usage of Quantum Approximate Optimization Algorithms (QAOAs) for test case selection by proposing QAOA-TCS. QAOAs merge the potential of gate-based quantum machines with the optimization capabilities of the adiabatic evolution. To prove the effectiveness of QAOAs for test case selection, we preliminarily investigate QAOA-TCS leveraging an ideal environment simulation before evaluating it on real quantum machines. Our results show that QAOAs perform better than the baseline algorithms in effectiveness while being comparable to SelectQA in terms of efficiency. These results encourage us to continue our experimentation with noisy environment simulations and real quantum machines.

Problem

• Optimizing test case selection for regression testing efficiency
• Reducing computational demands in large-scale software testing
• Comparing QAOA performance with classical and quantum methods

Innovation

• Uses QAOA for test case selection
• Simulates ideal quantum environment first
• Compares QAOA with SelectQA efficiency