Systolic Genetic Search for Software Engineering: The Test Suite Minimization Case

The Test Suite Minimization Problem (TSMP) is a \(\mathcal {NP}\)-hard real-world problem that arises in the field of software engineering. It lies in selecting the minimal set of test cases from a large test suite, ensuring that the test cases selected cover a given set of elements of a computer program under test. In this paper, we propose a Systolic Genetic Search (SGS) algorithm for solving the TSMP. We use the global concept of SGS to derive a particular algorithm to explicitly exploit the high degree of parallelism available in modern GPU architectures. The experimental evaluation on seven real-world programs shows that SGS is highly effective for the TSMP, as it obtains the optimal solution in almost every single run for all the tested software. It also outperforms two competitive Genetic Algorithms. The GPU-based implementation of SGS has achieved a high performance, obtaining runtime reductions of up to 40\(\times \) compared to its sequential implementation, and solving all the instances considered in less than nine seconds.