On the performance of EvoPSO: A PSO based algorithm for test data generation in EvoSuite

Mohammad Shahabi, S. Badiei, S. E. Beheshtian, R. Akbari, S. M. R. Moosavi
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引用次数: 3

Abstract

Nowadays software has a major role in our everyday life. Many critical tasks are done by software systems. The increasing complexity of software systems compels providing techniques and tools to design correct and well-functioning software in safety-critical systems. Up to 50% of the total software project costs are devoted to testing; hence, increased concern of automated software testing in recent years. The automation of software testing reduces costs and improves the effectiveness of tests that are generated in order to detect defects in the software under test. Various techniques are adopted for automated software testing including metaheuristic search algorithms. In this paper, we propose the EvoPSO algorithm using swarm intelligence paradigm. The algorithm is implemented in EvoSuite tool for the purpose of test data generation. The performance of EvoPSO has been investigated on SF110 dataset. The promising performance shows that EvoPSO is efficient and can give competitive results.
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EvoPSO的性能研究:基于PSO的EvoSuite测试数据生成算法
如今,软件在我们的日常生活中扮演着重要的角色。许多关键任务是由软件系统完成的。越来越复杂的软件系统迫使提供技术和工具来设计正确的和功能良好的软件在安全关键系统。高达软件项目总成本的50%用于测试;因此,近年来人们越来越关注自动化软件测试。软件测试的自动化降低了成本,并提高了为了检测被测软件中的缺陷而生成的测试的有效性。自动化软件测试采用了各种技术,包括元启发式搜索算法。在本文中,我们提出了基于群智能范式的EvoPSO算法。该算法在EvoSuite工具中实现,用于生成测试数据。在SF110数据集上研究了EvoPSO的性能。良好的性能表明,EvoPSO是高效的,可以提供有竞争力的结果。
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