Searchbased model transformation by example

Authors: Marouane Kessentini Houari Sahraoui Mounir Boukadoum Omar Ben Omar

Publish Date: 2010/09/22

Volume: 11, Issue: 2, Pages: 209-226

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Abstract

Model transformation MT has become an important concern in software engineering In addition to its role in modeldriven development it is useful in many other situations such as measurement refactoring and testcase generation Roughly speaking MT aims to derive a target model from a source model by following some rules or principles So far the contributions in MT have mostly relied on defining languages to express transformation rules However the task of defining expressing and maintaining these rules can be difficult especially for proprietary and nonwidely used formalisms In some situations companies have accumulated examples from past experiences Our work starts from these observations to view the transformation problem as one to solve with fragmentary knowledge ie with only examples of sourcetotarget MTs Our approach has two main advantages 1 it always proposes a transformation for a source model even when rule induction is impossible or difficult to achieve 2 it is independent from the source and target formalisms aside from the examples no extra information is needed In this context we propose an optimizationbased approach that consists of finding in the examples combinations of transformation fragments that best cover the source model To that end we use two strategies based on two searchbased algorithms particle swarm optimization and simulated annealing The results of validating our approach on industrial projects show that the obtained models are accurate

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