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Regression Test Selection for Testable Classes

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Dependable Computing - EDCC 5 (EDCC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 3463))

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Abstract

A reusable class must be tested many times: each time modifications are applied to it or its base classes; when a subclass is created, in which case the inherited and redefined features must be retested in the new context. Therefore, a class should be easy to test, specifically for test execution and results analysis, since these activities must be repeated often. Inspired by R. Binder’s self-testing class concept [4] we defined, in a previous work, a testable class as a 3-tuple: class implementation, class behavior model and built-in test (BIT) mechanisms. In this work we present how to use this information when a class is changed. Regression testing is necessary each time a software is changed, to assure that the modifications do not adversely affect the unchanged parts. It is assumed that the test suite applied when testing the old version is available for reuse. However, test suites can be large and require too much effort to be reapplied in their totality. In such cases, a subset of the tests must be selected. This selection usually requires extra information besides the source code. This work aims at answering the following question: how to use test information contained in a testable class to do regression testing? The answer involves, among other aspects, the definition of an approach to select tests for reuse. The approach can be fully automated and does not need the source code for regression-test selection.

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Martins, E., Vieira, V.G. (2005). Regression Test Selection for Testable Classes. In: Dal Cin, M., Kaâniche, M., Pataricza, A. (eds) Dependable Computing - EDCC 5. EDCC 2005. Lecture Notes in Computer Science, vol 3463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408901_33

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  • DOI: https://doi.org/10.1007/11408901_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25723-3

  • Online ISBN: 978-3-540-32019-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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