Model transformation testing: a bi‐level search‐based software engineering approach. Issue 11 (18th August 2015)

• Record Type: Journal Article
• Authors: Sahin, Dilan; Kessentini, Marouane; Wimmer, Manuel; Deb, Kalyanmoy
• Is Part Of: Journal of software. Volume 27:Issue 11(2015:Nov.)
• Page Start: 821
• Page End: 837
• Publication Date: 2015-08-18
• Subjects: search‐based software engineering -- software maintenance -- bi‐level optimization; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1735
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: The process of writing model transformations is a complex and error‐prone one. Thus, efficient techniques and tools for validating model transformations are needed. One of them is model transformation testing. The generation of test cases for model transformations is mainly based on metamodel and rules coverage criteria. In this paper, we propose to treat model transformation testing as a bi‐level optimization problem to combine the generation of test cases with mutation testing. In our adaptation, the upper‐level problem generates a set of test cases that maximizes the coverage of metamodels and errors introduced by the lower level to the transformation rules. The lower level maximizes the number of generated errors in the rules that cannot be detected by the test cases produced by the upper level. The main advantage of our bi‐level formulation is that the evaluation of test cases is not limited to the coverage of metamodels, but it allows evaluating their ability to detect errors. The statistical analysis of our experiments on different transformation mechanisms confirms the outperformance of our bi‐level proposal compared with state‐of‐the‐art model transformation testing techniques. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : The primary contributions of this paper can be summarized as follows: The paper introduces a novel formulation of model transformation testing as a bi‐level optimization problem. The paper reports the results of an empirical study with an implementation of our bi‐level approach. The obtained results provide evidence to support the claim that our proposal is more efficient, on average, than an existing technique based on metamodels coverage.