Discovering Model Transformation Pre-conditions using Automatically Generated Test Models

Abstract : Specifying a model transformation is challenging as it must be able to give a meaningful output for any input model in a possibly infinite modeling domain. Transformation preconditions constrain the input domain by rejecting input models that are not meant to be transformed by a model transformation. This paper presents a systematic approach to discover such preconditions when it is hard for a human developer to foresee complex graphs of objects that are not meant to be transformed. The approach is based on systematically generating a finite number of test models using our tool, PRAMANA to first cover the input domain based on input domain partitioning. Tracing a transformation's execution reveals why some preconditions are missing. Using a benchmark transformation from simplified UML class diagram models to RDBMS models we discover new preconditions that were not initially specified.
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Communication dans un congrès
IEEE International Symposium on Software Reliability Engineering, ISSRE 2015, Nov 2015, Washington DC, United States. Proceedings of IEEE International Symposium on Software Reliability Engineering, ISSRE 2015, 2015
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Jean-Marie Mottu, Sagar Sen, Juan Cadavid, Benoit Baudry. Discovering Model Transformation Pre-conditions using Automatically Generated Test Models. IEEE International Symposium on Software Reliability Engineering, ISSRE 2015, Nov 2015, Washington DC, United States. Proceedings of IEEE International Symposium on Software Reliability Engineering, ISSRE 2015, 2015. 〈hal-01228715〉

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