Optimization of model transformation output using genetic algorithm

Abstract

Model Driven Engineering is a revolutionary paradigm in Software Engineering, which reduces the complexity of development process by increasing the level of abstraction. Model transformation is a soul of MDE, being widely used to map one or more source model(s) into one or more target model(s). Finding an optimal result among a very large search space of possible output transformation models is an important issue in the community. In this paper a unified process for handling the large search space of model transformation results is presented. The process combines model transformation techniques and the genetic algorithm as a search based software engineering (SBSE) technique. The applicability and benefits of the approach is demonstrated using Class Responsibility Assignment (CRA) case study. © 2017 IEEE.