Discovering model transformation pre-conditions using automatically generated test models

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 pre-conditions 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 pre-conditions 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 pre-conditions are missing. Using a benchmark transformation from simplified UML class diagram models to RDBMS models we discover new pre-conditions that were not initially specified.