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Article
Regression Testing for Model Transformations: A Multi-Objective Approach
Lecture Notes in Computer Science
  • Jeffery Shelburg
  • Marouane Kessentini, Missouri University of Science and Technology
  • Daniel R. Tauritz, Missouri University of Science and Technology
Abstract

In current model-driven engineering practices, metamodels are modified followed by an update of transformation rules. Next, the updated transformation mechanism should be validated to ensure quality and robustness. Model transformation testing is a recently proposed effective technique used to validate transformation mechanisms. In this paper, a more efficient approach to model transformation testing is proposed by refactoring the existing test case models, employed to test previous metamodel and transformation mechanism versions, to cover new changes. To this end, a multi-objective optimization algorithm is employed to generate test case models that maximizes the coverage of the new metamodel while minimizing the number of test case model refactorings as well as test case model elements that have become invalid due to the new changes. Validation results on a widely used transformation mechanism confirm the effectiveness of our approach.

Meeting Name
5th International Symposium on Search-Based Software Engineering, SSBSE 2013 (2013: Aug. 24-26, St. Petersburg, Russian Federation)
Department(s)
Computer Science
Sponsor(s)
Missouri University of Science and Technology. Natural Computation Laboratory
Keywords and Phrases
  • Model Transformation,
  • Multi-Objective Optimization,
  • Search-Based Software Engineering,
  • Testing
International Standard Book Number (ISBN)
9783642397417
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2013 Springer Verlag, All rights reserved.
Publication Date
1-1-2013
Disciplines
Citation Information
Jeffery Shelburg, Marouane Kessentini and Daniel R. Tauritz. "Regression Testing for Model Transformations: A Multi-Objective Approach" Lecture Notes in Computer Science Vol. 8084 LNCS (2013) p. 209 - 223 ISSN: 0302-9743
Available at: http://works.bepress.com/daniel-tauritz/59/