Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context

Naohiko Tsuda, Hironori Washizaki, Yoshiaki Fukazawa, Yuichiro Yasuda, Shunsuke Sugimura

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Evolvability defects are non-understandable and non-modifiable states that do not directly produce runtime behavioral failures. Automatic source code evaluation by metrics and thresholds can help reduce the burden of a manual inspection. This study addresses two problems. (1) Evolvability defects are not usually managed in bug tracking systems. (2) Conventional methods cannot fully interpret the relations among the metrics in a given context (e.g., programming language, application domain). The key actions of our method are to (1) gather trainingdata for machine learning by experts' manual inspection of some of the files in given systems (benchmark) and (2) employ a classification-tree learner algorithm, C5.0, which can deal with non-orthogonal relations between metrics. Furthermore, we experimentally confirm that, even with less training-data, our method provides a more precise evaluation than four conventional methods (the percentile, Alves' method, Bender's method, and the ROC curve-based method).

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ83-94
ページ数12
ISBN(印刷版)9781538677575
DOI
出版ステータスPublished - 2018 8月 2
イベント18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018 - Lisbon, Portugal
継続期間: 2018 7月 162018 7月 20

出版物シリーズ

名前Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018

Other

Other18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018
国/地域Portugal
CityLisbon
Period18/7/1618/7/20

ASJC Scopus subject areas

  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理

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