Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case Generation

Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case Generation
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使用更少的资源进行测试:生成性能感知测试用例的自适应方法

DOI:
10.1109/tse.2019.2946773
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发表时间:
2019
影响因子:
7.4
通讯作者:
Sebastiano Panichella
Sebastiano Panichella
中科院分区:
计算机科学1区
文献类型:
--
作者:
Giovanni Grano;Christoph Laaber;Annibale Panichella;Sebastiano Panichella

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自动测试案例生成是一种有效的技术,可以产生高覆盖的测试套件,而大多数研究工作已致力于满足覆盖范围的标准,这是一种在此方面出现的趋势。是两个基本维度:较低的测试减少了生成过程的资源需求,并以后的回归测试阶段。一代需要解决两个主要的挑战:提供最小的间接费用的资源使用量,并避免对最终覆盖范围和故障检测效果的影响,以应对这些挑战。 - 这提供了对测试执行成本(即运行时和内存使用量)的合理估计,我们提出了一种自适应策略,称为Adynamosa这些代理通过扩展Dynamosa,这是一种单位测试中的最新进化算法。与Dynamosa相比,ADYNAMOSA与Dynamosa相比,在七个不同的覆盖范围标准上,堆的记忆消耗(-15%)。类似的错误检测有效性。
Automated test case generation is an effective technique to yield high-coverage test suites. While the majority of research effort has been devoted to satisfying coverage criteria, a recent trend emerged towards optimizing other non-coverage aspects. In this regard, runtime and memory usage are two essential dimensions: less expensive tests reduce the resource demands for the generation process and later regression testing phases. This study shows that performance-aware test case generation requires solving two main challenges: providing a good approximation of resource usage with minimal overhead and avoiding detrimental effects on both final coverage and fault detection effectiveness. To tackle these challenges, we conceived a set of performance proxies—inspired by previous work on performance testing— that provide a reasonable estimation of the test execution costs (i.e., runtime and memory usage). Thus, we propose an adaptive strategy, called aDynaMOSA, which leverages these proxies by extending DynaMOSA, a state-of-the-art evolutionary algorithm in unit testing. Our empirical study —involving 110 non-trivial Java classes—reveals that our adaptive approach generates test suite with statistically significant improvements in runtime (−25 percent) and heap memory consumption (−15 percent) compared to DynaMOSA. Additionally, aDynaMOSA has comparable results to DynaMOSA over seven different coverage criteria and similar fault detection effectiveness. Our empirical investigation also highlights that the usage of performance proxies (i.e., without the adaptiveness) is not sufficient to generate more performant test cases without compromising the overall coverage.
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