Design pattern detection using software metrics and machine learning

Satoru Uchiyama, Hironori Washizaki, Yoshiaki Fukazawa, Atsuto Kubo

研究成果: Conference article

6 引用 (Scopus)

抜粋

The understandability, maintainability, and reusability of object-oriented programs could be improved by automatically detecting well-known design patterns in programs. Many existing detection techniques are based on static analysis and use strict conditions composed of class structure data. Hence, it is difficult for them to detect design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. We propose a design pattern detection technique using metrics and machine learning. Our technique judges candidates for the roles that compose the design patterns by using machine learning and measurements of metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. We conducted experiments that showed that our technique was more accurate than two previous techniques.

元の言語English
ページ(範囲)38-47
ページ数10
ジャーナルCEUR Workshop Proceedings
708
出版物ステータスPublished - 2011 12 1
イベントJoint 1st Int. Workshop on Model-Driven Software Migration, MDSM 2011 and the 5th International Workshop on Software Quality and Maintainability, SQM 2011 - Workshops at the 15th European Conf. on Software Maintenance and Reengineering, CSMR 2011 - Oldenburg, Germany
継続期間: 2011 3 12011 3 1

ASJC Scopus subject areas

  • Computer Science(all)

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