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Towards an automation of the mutation analysis dedicated to model transformation
Author(s) -
Aranega Vincent,
Mottu JeanMarie,
Etien Anne,
Degueule Thomas,
Baudry Benoit,
Dekeyser JeanLuc
Publication year - 2014
Publication title -
software testing, verification and reliability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 49
eISSN - 1099-1689
pISSN - 0960-0833
DOI - 10.1002/stvr.1532
Subject(s) - computer science , traceability , transformation (genetics) , mutation testing , mutation , automation , software engineering , set (abstract data type) , porting , model transformation , data mining , test case , programming language , software , artificial intelligence , machine learning , engineering , mechanical engineering , biochemistry , chemistry , regression analysis , consistency (knowledge bases) , gene
Summary A benefit of model‐driven engineering relies on the automatic generation of artefacts from high‐level models through intermediary levels using model transformations. In such a process, the input must be well designed, and the model transformations should be trustworthy. Because of the specificities of models and transformations, classical software test techniques have to be adapted. Among these techniques, mutation analysis has been ported, and a set of mutation operators has been defined. However, it currently requires considerable manual work and suffers from the test data set improvement activity. This activity is a difficult and time‐consuming job and reduces the benefits of the mutation analysis. This paper addresses the test data set improvement activity. Model transformation traceability in conjunction with a model of mutation operators and a dedicated algorithm allow to automatically or semi‐automatically produce improved test models. The approach is validated and illustrated in two case studies written in Kermeta.Copyright © 2014 John Wiley & Sons, Ltd.