Generating Test Data for Dynamically Discovering Likely Program Invariants
Generating Test Data for Dynamically Discovering Likely Program Invariants
复制标题
生成测试数据以动态发现可能的程序不变量
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Neelam Gupta
中科院分区：
文献类型：
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作者：
Neelam Gupta
程序不变的动态检测正在成为一个重要的研究领域，并且存在许多具有挑战性的问题。与任何动态方法一样，动态发现可能不变的精度取决于用于检测不变性的测试用例的质量。因此，生成支持准确检测程序不变剂的合适测试用例对于不变检测的动态方法至关重要。在本文中，我们探讨了使用现有的测试数据生成技术来提高动态检测到的程序不变性的准确性的新方向。首先，我们讨论现有测试套件的增强，以提高动态发现的不变性的准确性。如果要在运行时监视其值的变量和表达式，则可以在运行动态分析之前进行测试套件的增强。另一方面，可以首先使用可用的测试套件进行动态分析，以获得程序不变的初始猜测。然后可以使用这些猜测的不变性来生成测试用例以增强测试套件。我们还建议使用现有的测试数据生成技术来提高使用已经可用的测试套件猜测的不变性的准确性。
Dynamic detection of program invariants is emerging as an important research area with many challenging problems. As with any dynamic approach, the accuracy of dynamic discovery of likely program invariants depends on the quality of test cases used to detect invariants. Therefore, generating suitable test cases that support accurate detection of program invariants is crucial to the dynamic approach for invariant detection. In this paper, we explore new directions in using the existing test data generation techniques to improve the accuracy of dynamically detected program invariants. First we discuss the augmentation of existing test suites to improve the accuracy of dynamically discovered invariants. The augmentation of the test suite may be done prior to running the dynamic analysis if the variables and expressions whose values will be monitored at runtime are known in advance. On the other hand, the dynamic analysis may be run first using an available test suite to obtain an initial guess of program invariants. These guessed invariants may then be used to generate test cases to augment the test suite. We also propose the use of existing test data generation techniques in improving the accuracy of invariants guessed using an already available test suite.