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<p>The software test case optimization problem is a challenging task that involves selecting a critical subset of test cases from a larger test suite. To address this problem, metaheuristic-based optimization approaches have been found more suitable due to their capability to effectively exploit and explore complex and large search spaces. In this paper, we adapt the energy valley optimizer—a physics-inspired search algorithm—to the domain of software testing and propose a customized approach named the energy valley optimizer-based software test case optimizer (EVO–STCO). To evaluate its effectiveness, EVO–STCO was applied to 41 diverse and complex software projects. The obtained results were also compared with those of four established metaheuristic algorithms: particle swarm optimization, harmony search algorithm, genetic algorithm, and ant colony optimization. The comparative results prove the superiority of EVO–STCO in terms of the MCV and WCV metrics, which measure test case effectiveness and robustness. These results underscore the value of the proposed EVO–STCO approach as an effective tool for developers in optimizing test cases.</p>

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Energy valley optimizer-based metaheuristic approach for solving software test case optimization problem

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Updesh Kumar Jaiswal,
  • Jaishree Jain,
  • Eshank Jain,
  • Harnit Saini,
  • Pancham Singh

摘要

The software test case optimization problem is a challenging task that involves selecting a critical subset of test cases from a larger test suite. To address this problem, metaheuristic-based optimization approaches have been found more suitable due to their capability to effectively exploit and explore complex and large search spaces. In this paper, we adapt the energy valley optimizer—a physics-inspired search algorithm—to the domain of software testing and propose a customized approach named the energy valley optimizer-based software test case optimizer (EVO–STCO). To evaluate its effectiveness, EVO–STCO was applied to 41 diverse and complex software projects. The obtained results were also compared with those of four established metaheuristic algorithms: particle swarm optimization, harmony search algorithm, genetic algorithm, and ant colony optimization. The comparative results prove the superiority of EVO–STCO in terms of the MCV and WCV metrics, which measure test case effectiveness and robustness. These results underscore the value of the proposed EVO–STCO approach as an effective tool for developers in optimizing test cases.