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Coupling based integration testing: A fitness function

机译:基于耦合的集成测试:适应度函数

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Test data generation is one of the most important and crucial phases in software testing. Software testing is not possible without adequate test data. Many automated and manual test data generation techniques have been proposed for software testing. Most of the work on automated software test data generation at unit level is by applying evolutionary approaches for test data generation. Evolutionary approaches, especially genetic algorithm, use fitness function for evaluation of individuals in different iterations. In this paper, we have proposed a novel fitness function for test data generation at integration level. Fitness function plays a vital in the success of evolutionary testing, without effective fitness function evolutionary testing is not effective for achieving required results. The success of evolutionary testing depends upon the success of fitness function. We have proposed a novel fitness function for coupling based integration testing. Up until now, there is no fitness function that caters coupling based integration for test data generation. Up till now most of the work for test data generation is at unit level and fitness function also cater only unit level test data generation. We have implemented our fitness function in a prototype tool ‘EE-COUP’ and performed different experiments for test data generation for some sample programs containing coupling relationship.
机译:测试数据生成是软件测试中最重要和至关重要的阶段之一。没有足够的测试数据,就不可能进行软件测试。已经提出了许多用于软件测试的自动和手动测试数据生成技术。在单元级别上自动软件测试数据生成的大多数工作是通过应用进化方法来生成测试数据。进化方法,尤其是遗传算法,使用适应度函数来评估不同迭代中的个体。在本文中,我们提出了一种新的适应度函数,用于在集成级别生成测试数据。适应度函数对于成功进行进化测试至关重要,如果没有有效的适应度函数,进化测试就无法有效地达到所需的结果。进化测试的成功取决于适应度函数的成功。我们提出了一种新颖的适应度函数,用于基于耦合的集成测试。到目前为止,还没有适合于基于耦合的集成来生成测试数据的适应性功能。到目前为止,用于测试数据生成的大部分工作都是在单位级别进行的,适应性功能也只能满足单位级别的测试数据生成。我们已经在原型工具“ EE-COUP”中实现了适应性功能,并对包含耦合关系的一些示例程序执行了不同的实验,以生成测试数据。

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