Abstract
Background: Model transformations play a key role in Model-Driven Engineering (MDE). Testing model transformation is an important activity to ensure the quality and correctness of the generated models. However, during the evolution and maintenance of these model transformation programs, frequently testing them by running a large number of test cases can be costly. Regression test selection is a form of testing, which selects tests from an existing test suite to test a modified program. Aim: The aim of the paper is to present a test selection approach for the regression testing of model transformations. The selected test case suite should be smaller in size than the full test suite, thereby reducing the testing overhead, while at the same time the fault detection capability of the full test suite should not be compromised. Method: approach is based on the use of a traceability mapping of test cases with their corresponding rules to select the affected test items. The approach is complemented with a tool that automates the proposed process. Results: Our experiments show that the proposed approach succeeds in reducing the size of the selected test case suite, and hence its execution time, while not compromising the fault detection capability of the full test suite. Conclusion: The experimental results confirm that our regression test selection approach is cost-effective compared to a retest strategy.
Highlights
Model-Driven Engineering (MDE) refers to representing, designing, and developing a system in the form of models [1]
We present a framework for the regression testing of model transformations, which is based on the use of a metamodel that links test cases to their corresponding test items and test artifacts
If the results of our experiments show no benefit of using our approach, we can conclude that our approach is not needed and a tester would be better off rerunning all test cases in a test suite. – RQ2: How cost-effective is our approach? We want to ensure that the fault detection capability when rerunning a selected set of test cases is not compromised, while at the same time there is a significant saving in the overhead involved when running these test cases
Summary
Model-Driven Engineering (MDE) refers to representing, designing, and developing a system in the form of models [1]. Several techniques for testing model transformations have been proposed in the literature [5, 6] These techniques generally require executing a large number of test cases to ensure the desired coverage criteria. This can be time-consuming and may require days or even weeks to complete. During the evolution and maintenance of these model transformation programs, frequently testing them by running a large number of test cases can be costly. Results: Our experiments show that the proposed approach succeeds in reducing the size of the selected test case suite, and its execution time, while not compromising the fault detection capability of the full test suite. Conclusion: The experimental results confirm that our regression test selection approach is cost-effective compared to a retest strategy
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