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search-based Cost-Effective Software Remodularization

Journal of Computer Science & Technology 2018年第6期33卷 1320-1336页

作者： Rim Mahouachi Complex Outstanding Systems Modeling Optimization and Supervision Laboratory National School of Computer Science University of Manouba 2010 Manouba Tunisia

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.

关键词： remodularization search-based software engineering refactoring effort multi-objective optimization seman-tics dependency

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A Genetic Approach to Analyze Algorithm Performance based on the Worst-Case Instances

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Journal of Software Engineering and Applications 2010年第8期3卷 767-775页

作者： So-Yeong Jeon Yong-Hyuk Kim 不详

search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.

关键词： search-based Software Engineering Automated Test Data Generation Worst-Case Instance Algorithm

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