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Discrete and combinatorial gravitational search algorithms for test case prioritization and minimization
International Journal of Information Technology Pub Date : 2021-03-07 , DOI: 10.1007/s41870-021-00628-8
Anu Bajaj , Om Prakash Sangwan

Regression testing is an essential but expensive activity to re-execute all the test cases every time the software updates. Test case prioritization and minimization reduces the cost and efforts required for retesting by prioritizing the test cases based on their importance and minimizing the redundancy. Optimization approaches further enhance the effectiveness of these techniques. In this paper, a discrete and combinatorial gravitational search algorithm is proposed to solve the test case prioritization and minimization problems. Furthermore, an improved version is developed using the chaotic map to update the gravitational constant. The proposed algorithms are compared with the most commonly used algorithm, i.e., genetic algorithm. Three subject programs of varying sizes are used for evaluation. Simulation results prove that the proposed algorithms are more efficient and effective than the genetic algorithm for test case prioritization and minimization. Statistical representation via boxplots of APFD and interval plots of minimized suite size performance metrics, confirms that the improved gravitational search algorithm with chaotic gravitational constant has a more squeezed distribution than the standard gravitational search algorithm.



中文翻译:

离散和组合引力搜索算法,用于测试用例的优先级划分和最小化

回归测试是每次软件更新时都要重新执行所有测试用例的一项重要但昂贵的活动。通过对测试用例进行优先级排序和最小化,可以根据其重要性对测试用例进行优先级排序并最大程度地减少冗余,从而降低了重新测试所需的成本和工作量。优化方法进一步增强了这些技术的有效性。本文提出了一种离散组合的引力搜索算法来解决测试用例的优先级排序和最小化问题。此外,使用混沌映射图开发了改进版本以更新重力常数。将提出的算法与最常用的算法(即遗传算法)进行比较。评估使用了三个不同大小的主题程序。仿真结果表明,所提出的算法比遗传算法在测试用例的优先级和最小化方面更加有效。通过APFD的箱形图和最小化套件尺寸性能指标的区间图的统计表示,证实了具有混沌引力常数的改进引力搜索算法比标准引力搜索算法具有更紧凑的分布。

更新日期:2021-03-07
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