AuthorsM. Bowman, L. Briand and Y. Labiche
TitleMulti-Objective Genetic Algorithms to Support Class Responsibility Assignment
StatusAccepted
Publication TypeProceedings, refereed
Year of Publication2007
Conference NameInternational Conference on Software Maintenance (ICSM 2007)
Abstract

Class responsibility assignment is not an easy skill to acquire. There is ample evidence that this is hard to teach and apply. Though there are many methodologies for assigning responsibilities to classes, they all rely on human judgment and decision making. In this paper, our objective is to provide decision-making help to re-assign methods and attributes to classes in a class diagram. Our solution is based on a multi-objective genetic algorithm (MOGA) and uses class coupling and cohesion measurement. Our MOGA takes as input a class diagram to be optimized, typically produced during the analysis phase of software development and evolution (i.e., a domain model) in the context of Model-Driven Development, and suggests possible improvements to the diagram. The choice of a MOGA stems from the fact that there are typically many evaluation criteria that cannot be easily combined into one objective, and several alternative solutions are acceptable for a given OO domain model. This article presents our approach in details, our decisions regarding the multi-objective genetic algorithm, and reports on a case study. Our results suggest that the MOGA can help correct suboptimal class responsibility assignment decisions.

Citation KeySimula.SE.146