Software modularization is a technique used to divide a software system into independent modules （packages）that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet ...Software modularization is a technique used to divide a software system into independent modules （packages）that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet new require-ments, their modularizations become complex and gradually loose their quality. Thus, it is challenging to automaticallyoptimize the classes＇ distribution in packages, also known as remodularization. To alleviate this issue, we introduce a newapproach to optimize software modularization by moving classes to more suitable packages. In addition to improving designquality and preserving semantic coherence, our approach takes into consideration the refactoring effort as an objective initself while optimizing software modularization. We adapt the Elitist Non-dominated Sorting Genetic Algorithm （NSGA-Ⅱ）of Deb et al. to find the best sequence of refactorings that 1） maximize structural quality, 2） maximize semantic cohesivenessof packages （evaluated by a semantic measure based on WordNet）, and 3） minimize the refactoring effort. We report theresults of an evaluation of our approach using open-source projects, and we show that our proposal is able to produce acoherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer＇s pointsof view.展开更多
Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality criteria.Ho...Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality criteria.However,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software systems.Therefore,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization solution.Additionally,such modularization can be good from the quality metrics perspective but may not be acceptable to the developers.To produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization objectives.Further,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization solution.To assess the effectiveness of the proposed approach,we applied it over five software projects.The obtained remodularization solutions are evaluated with the software quality metrics and developers view of remodularization.Results demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.展开更多
文摘Software modularization is a technique used to divide a software system into independent modules （packages）that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet new require-ments, their modularizations become complex and gradually loose their quality. Thus, it is challenging to automaticallyoptimize the classes＇ distribution in packages, also known as remodularization. To alleviate this issue, we introduce a newapproach to optimize software modularization by moving classes to more suitable packages. In addition to improving designquality and preserving semantic coherence, our approach takes into consideration the refactoring effort as an objective initself while optimizing software modularization. We adapt the Elitist Non-dominated Sorting Genetic Algorithm （NSGA-Ⅱ）of Deb et al. to find the best sequence of refactorings that 1） maximize structural quality, 2） maximize semantic cohesivenessof packages （evaluated by a semantic measure based on WordNet）, and 3） minimize the refactoring effort. We report theresults of an evaluation of our approach using open-source projects, and we show that our proposal is able to produce acoherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer＇s pointsof view.
文摘Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality criteria.However,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software systems.Therefore,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization solution.Additionally,such modularization can be good from the quality metrics perspective but may not be acceptable to the developers.To produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization objectives.Further,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization solution.To assess the effectiveness of the proposed approach,we applied it over five software projects.The obtained remodularization solutions are evaluated with the software quality metrics and developers view of remodularization.Results demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.
