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Automated Test Data Generation for Coupling Based Integration Testing of Object Oriented Programs Using Particle Swarm Optimization (PSO)

机译:自动测试数据生成,用于使用粒子群优化(PSO)基于对象面向程序的集成测试

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Automated test data generation is a challenging problem for researchers in the area of software testing. Up until now, most of the work on test data generation is at unit level. Until level test data generation involves the execution of test path at unit level where interaction with other components is minimum. Test data generation for unit testing involves a single path and there is no usage of formal and actual parameters. The problem of automated test data generation becomes very challenging when we move to other levels of testing including integration testing and system level testing. At integration level, the variables are passed as arguments to other components and variables change their names; also multiple paths are executed from different components to ensure proper functionality. Recently evolutionary approaches have been proven a powerful tool for test data generation. In this paper, we have proposed a novel approach for test data generation for coupling based integration testing using particle swarm optimization. Up until now, there is no research for test data generation for coupling based integration testing using particle swarm optimization. Our approach takes the coupling path as input, containing different sub paths, and generates the test data using particle swarm optimization. We have also proposed architecture of tool for automation of our approach. In future, we will implement our proposed approach and will perform different experiments to prove its significance.
机译:自动化测试数据生成对于软件测试领域的研究人员来说是一个具有挑战性的问题。到目前为止,大多数测试数据生成的工作都处于单位级别。直到级别测试数据生成涉及在与其他组件的交互最小的单位级别执行测试路径。用于单元测试的测试数据生成涉及单个路径,并且没有正式和实际参数的使用情况。当我们移动到其他级别的测试时,自动化测试数据生成的问题变得非常具有挑战性,包括集成测试和系统级测试。在集成级别,变量作为其他组件的参数传递,变量更改其名称;还从不同的组件执行多条路径,以确保正常功能。最近,进化方法已被证明是一个有效的测试数据生成工具。在本文中,我们提出了一种用于使用粒子群优化耦合基于集成测试的测试数据生成的新方法。到目前为止,目前尚未使用粒子群优化耦合基于集成测试的测试数据生成的研究。我们的方法将耦合路径作为输入,包含不同的子路径,并使用粒子群优化生成测试数据。我们还提出了用于自动化我们的方法的工具体系结构。未来,我们将实施我们提出的方法,并将进行不同的实验以证明其重要性。

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