Decomposing class responsibilities using distance-based method similarity
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  • 作者:Junha Lee ; Dae-Kyoo Kim ; Suntae Kim ; Sooyong Park
  • 刊名:Frontiers of Computer Science in China
  • 出版年:2016
  • 出版时间:August 2016
  • 年:2016
  • 卷:10
  • 期:4
  • 页码:612-630
  • 全文大小:2,096 KB
  • 刊物类别:Computer Science
  • 刊物主题:Computer Science, general
    Chinese Library of Science
  • 出版者:Higher Education Press, co-published with Springer-Verlag GmbH
  • ISSN:1673-7466
  • 卷排序:10
文摘
Cohesion is a design quality that has a great impact on the posterior development and maintenance. As software evolves, the cohesion of the system becomes weaker due to the changes introduced during evolution. Over evolution, a single responsibility class may be unintentionally assigned other responsibilities, which makes the class less cohesive and more complex and consequently increases the complexity of the entire system. There has been much work on decomposing class responsibilities based on internal class relationships such as method-attribute referencing and internal method calls. However, object-oriented systems involve significant external class relationships carrying important behavioral semantics, which should be taken into account in identifying class responsibilities. In this paper, we present a novel approach for identifying and decomposing classes responsibilities based on method similarity using both internal and external class relationships.We extend the existing work for measuring similarity of internal class relationships and present a distance-based method for measuring external class relationships. We evaluate the approach using three open source applications — JMeter, JHotDraw, and ArgoUML. The evaluation shows that the presented approach improves precision over the existing work. We validate the results using independent samples T-test and ANOVA applied to a set of hypotheses. The validation confirms that the results are statistically significant.Keywordsclass decompositioncohesionexternal relationshipsidentifying responsibilitymethod similarity

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