任务的适应度函数:基于搜索的检测真实故障的测试套件生成

Gregory Gay
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引用次数: 32

摘要

基于搜索的测试生成,如果在故障检测中有效,可以降低测试成本。这种技术依赖于适应度函数来指导搜索。最终,这些函数代表了测试目标,它们近似于——但不保证——故障检测。依赖近似的需要导致了两个问题——适应度函数能产生有效的测试吗?如果可以,应该使用哪个来生成测试?为了回答这些问题,我们评估了EvoSuite框架的故障检测能力及其八个适合度函数对来自Defects4J数据库的353个实际故障的检测能力。我们的分析发现,效率的最强指标是高水平的代码覆盖率。因此,分支覆盖适应度函数是最有效的。我们的研究结果表明,应该使用彻底探索系统结构的适应度函数作为主要的生成目标,并辅以改变所探索场景的次级适应度函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Fitness Function for the Job: Search-Based Generation of Test Suites That Detect Real Faults
Search-based test generation, if effective at fault detection, can lower the cost of testing. Such techniques rely on fitness functions to guide the search. Ultimately, such functions represent test goals that approximate — but do not ensure — fault detection. The need to rely on approximations leads to two questions — can fitness functions produce effective tests and, if so, which should be used to generate tests? To answer these questions, we have assessed the fault-detection capabilities of the EvoSuite framework and eight of its fitness functions on 353 real faults from the Defects4J database. Our analysis has found that the strongest indicator of effectiveness is a high level of code coverage. Consequently, the branch coverage fitness function is the most effective. Our findings indicate that fitness functions that thoroughly explore system structure should be used as primary generation objectives — supported by secondary fitness functions that vary the scenarios explored.
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