An Empirical Study on Clustering-based Quantum Approximate Optimization Algorithm for Regression Test Case Selection

Regression testing assesses whether software components that have already been developed and tested continue to function as expected after modifications. The ideal regression testing approach re-executes the whole test suite after each modification, which is unfeasible in real-world scenarios due to its enormous computational burden. Therefore, optimized approaches have been developed, also referred to as Test Case Selection. The state of the art in regression testing optimization includes both classical approaches, such as Additional Greedy and DIV-GA, and quantum approaches, such as SelectQA, BootQA, and IGDec-QAOA. However, while the former incur high computational costs, the latter are constrained by the physical limitations of currently available quantum machines. This paper extends the evaluation of QAOA-TCS, our quantum approach to solving the test case selection problem. The objective is to thoroughly evaluate its performance and compare it with state-of-the-art classical and quantum approaches, as well as its robustness to quantum noise. The results show that QAOA-TCS outperforms classical state-of-the-art approaches in most cases, is comparable to quantum state-of-the-art approaches, and maintains stable performance even under high noise levels. These findings encourage further experiments on real quantum devices and provide substantial steps toward the application of quantum algorithms in software engineering.