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Automatic Generation of Object-Oriented Tests With a Multistage-Based Genetic Algorithm

机译:以多级基于遗传算法自动生成面向对象的测试

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—The widespread use of the object-oriented programs (OOPs) makes the requirement for testsgeneration strategies for testing the OOPs increases from day to day. In this paper, we present a multi-stage genetic algorithm (MSGA) to generate a suite of tests for testing the OOPs. MSGA includes two optimization stages. The first stage concentrates on finding test cases (sequences of called methods), which satisfy a given test criterion. The second stage focuses on generating test data (values of the arguments of the called methods). In addition, we introduce a new chromosome representation, which consists of two concatenated one-dimensional arrays. Each array contains set of homogeneous genes. In addition, we introduce set of strategies for encoding and decoding the tests. Furthermore, we present set of new genetic operators and the required pre- and post-conditions for applying these operators. In order to determine the applicability and practicability of MSGA, we introduce a new testing tool by implementing MSGA. Also, we conduct a case study by the new tool to assess the efficiency of MSGA in data-flow testing of OOPs.
机译:- 面向对象的程序(oops)的广泛使用使得测试的测试要求测试oops从日期到日期增加。在本文中,我们介绍了一种多级遗传算法(MSGA),以生成一套测试oops的测试。 MSGA包括两个优化阶段。第一阶段专注于发现测试用例(称为方法的序列),其满足给定的测试标准。第二阶段侧重于生成测试数据(被叫方法的参数的值)。此外,我们介绍了一种新的染色体表示,由两个连接的一维阵列组成。每个阵列包含一组均匀基因。此外,我们介绍了一组用于编码和解码测试的策略。此外,我们展示了一套新的遗传运营商以及应用这些运营商的所需的预算和后期条件。为了确定Msga的适用性和实用性,我们通过实施MSGA来引入新的测试工具。此外,我们开展了新工具的案例研究,以评估Msga在oops的数据流测试中的效率。

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