Discovering Model Transformation Pre-conditions using Automatically Generated Test Models

Jean-Marie Mottu (1) , Sagar Sen (2) , Juan Cadavid (3) , Benoit Baudry (4)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 SRL - Simula Research Laboratory [Lysaker]
3 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies
4 DiverSe - Diversity-centric Software Engineering

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.

Mots clés: Model transformation; Testing; Model Generation; Pre-condition; Incomplete Domain Specification

Domaines: Génie logiciel [cs.SE]

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⟩