Model refactoring using examples: a search‐based approach

摘要

One of the important challenges in model‐driven engineering is how to improve the quality of the models' design in order to help designers understand them. Refactoring represents an efficient technique to improve the quality of a design while preserving its behavior. Most of existing work on model refactoring relies on declarative rules to detect refactoring opportunities and to apply the appropriate refactorings. However, a complete specification of refactoring opportunities requires a huge number of rules. In this paper, we consider the refactoring mechanism as a combinatorial optimization problem where the goal is to find good refactoring suggestions starting from a small set of refactoring examples applied to similar contexts. Our approach, named model refactoring by example, takes as input an initial model to refactor, a set of structural metrics calculated on both initial model and models in the base of examples, and a base of refactoring examples extracted from different software systems and generates as output a sequence of refactorings. A solution is defined as a combination of refactoring operations that should maximize as much as possible the structural similarity based on metrics between the initial model and the models in the base of examples. A heuristic method is used to explore the space of possible refactoring solutions. To this end, we used and adapted a genetic algorithm as a global heuristic search. The validation results on different systems of real‐world models taken from open‐source projects confirm the effectiveness of our approach. Copyright © 2014 John Wiley & Sons, Ltd.