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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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https://hal.archives-ouvertes.fr/hal-01228715
Contributor : Jean-Marie Mottu <>
Submitted on : Friday, November 13, 2015 - 4:04:26 PM
Last modification on : Monday, February 10, 2020 - 6:12:34 PM
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  • HAL Id : hal-01228715, version 1

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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. ⟨hal-01228715⟩

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