Multi-Objective Genetic Algorithm to Support Class Responsibility Assignment Export

@inproceedings{bowman07classresponsibility, abstract = {Class responsibility assignment is not an easy skill to acquire. Though there are many methodologies for assigning responsibilities to classes, they all rely on human judgment and decision making. 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 and suggests possible improvements to it. 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 detail, 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.}, author = {Bowman, M. and Briand, L. C. and Labiche, Y.}, booktitle = {IEEE International Conference on Software Maintenance (ICSM)}, citeulike-article-id = {3992107}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICSM.2007.4362625}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4362625}, doi = {10.1109/ICSM.2007.4362625}, journal = {IEEE International Conference on Software Maintenance (ICSM)}, keywords = {assignment-problem, clustering, genetic-algorithm, multiobjective, sbse}, pages = {124--133}, posted-at = {2009-02-01 20:06:42}, priority = {2}, title = {Multi-Objective Genetic Algorithm to Support Class Responsibility Assignment}, url = {http://dx.doi.org/10.1109/ICSM.2007.4362625}, year = {2007} }

TY - CONF ID - bowman07classresponsibility L3 - citeulike-article-id:3992107 N2 - Class responsibility assignment is not an easy skill to acquire. Though there are many methodologies for assigning responsibilities to classes, they all rely on human judgment and decision making. 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 and suggests possible improvements to it. 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 detail, 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. JF - IEEE International Conference on Software Maintenance (ICSM) EP - 133 TI - Multi-Objective Genetic Algorithm to Support Class Responsibility Assignment T2 - IEEE International Conference on Software Maintenance (ICSM) SP - 124 KW - assignment-problem KW - clustering KW - genetic-algorithm KW - multiobjective KW - sbse AU - Bowman, M AU - Briand, LC AU - Labiche, Y PY - 2007/// UR - http://dx.doi.org/10.1109/ICSM.2007.4362625 ER -