論文

2021年

The seven information features of class for blob and feature envy smell detection in a class diagram

Proceedings of International Conference on Artificial Life and Robotics
  • Bayu Priyambadha
  • ,
  • Tetsuro Katayama
  • ,
  • Yoshihiro Kita
  • ,
  • Hisaaki Yamaba
  • ,
  • Kentaro Aburada
  • ,
  • Naonobu Okazaki

2021
開始ページ
348
終了ページ
351
記述言語
掲載種別
研究論文(国際会議プロシーディングス)

Measuring the quality of software design artifacts is difficult due to the limitation of information in the design phase. The class diagram is one of the design artifacts produced during the design phase. The syntactic and semantic information in the class is important to consider in the measurement process. The class-related information is used to detect the smell as an indicator of a lack of quality. All information related to the class is used by several classifiers to prove how informative it to be used to detect the smell. The smell types that are a concern in this research are Blob and Feature Envy. The experiment using three classifiers (j48, Multi-Layer Perceptron, and Naïve Bayes) confirms that the information can be used to detect Blob smell, on the other hand, Feature Envy, still needs more research. The average true positive rate of each classifier is about 80.67%.

リンク情報
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108807237&origin=inward
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ID情報
  • eISSN : 2435-9157
  • SCOPUS ID : 85108807237

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