An Empirical Study on Clustering-based Quantum Approximate Optimization Algorithm for Regression Test Case Selection
[Inżynieria oprogramowania] Autorzy: Antonio Trovato, Martin Beseda, Dario Di Nucci
Regression testing assesses whether software components that have already been developed and tested continue to work correctly after modifications. Ideally, regression testing re-executes the entire test suite after each modification, but this is infeasible in real-world scenarios because of the computational burden; therefore, optimized approaches, such as Test Case Selection, have been developed. 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 for the test case selection problem. We thoroughly evaluate its performance, co…
- Kategoria arxiv
- cs.SE
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- Inżynieria oprogramowania