IEEE Standard Computer Dictionary: Compilation of IEEE Standard Computer Glossaries
IEEE Standard Computer Dictionary: Compilation of IEEE Standard Computer Glossaries
On the Effectiveness of Mutation Analysis as a Black Box Testing Technique
ASWEC '01 Proceedings of the 13th Australian Conference on Software Engineering
MuJava: an automated class mutation system: Research Articles
Software Testing, Verification & Reliability
From genetic to bacteriological algorithms for mutation-based testing: Research Articles
Software Testing, Verification & Reliability
On Combining Multi-formalism Knowledge to Select Models for Model Transformation Testing
ICST '08 Proceedings of the 2008 International Conference on Software Testing, Verification, and Validation
A transformational language for mutant description
Computer Languages, Systems and Structures
Towards traceable test-driven development
TEFSE '09 Proceedings of the 2009 ICSE Workshop on Traceability in Emerging Forms of Software Engineering
Using Traceability to Enhance Mutation Analysis Dedicated to Model Transformation
MODEVVA '10 Proceedings of the 2010 Workshop on Model-Driven Engineering, Verification, and Validation
An Analysis and Survey of the Development of Mutation Testing
IEEE Transactions on Software Engineering
Model transformations in practice workshop
MoDELS'05 Proceedings of the 2005 international conference on Satellite Events at the MoDELS
Weaving executability into object-oriented meta-languages
MoDELS'05 Proceedings of the 8th international conference on Model Driven Engineering Languages and Systems
Mutation analysis testing for model transformations
ECMDA-FA'06 Proceedings of the Second European conference on Model Driven Architecture: foundations and Applications
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Model transformation can't be directly tested using program techniques. Those have to be adapted to model characteristics. In this paper we focus on one test technique: mutation analysis. This technique aims to qualify a test data set by analyzing the execution results of intentionally faulty program versions. If the degree of qualification is not satisfactory, the test data set has to be improved. In the context of model, this step is currently relatively fastidious and manually performed. We propose an approach based on traceability mechanisms in order to ease the test model set improvement in the mutation analysis process. We illustrate with a benchmark the quick automatic identification of the input model to change. A new model is then created in order to raise the quality of the test data set.