Identifying recurring association rules in software defect prediction

Takashi Watanabe, Akito Monden, Yasutaka Kamei, Shuji Morisaki

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

Association rule mining discovers patterns of co-occurrences of attributes as association rules in a data set. The derived association rules are expected to be recurrent, that is, the patterns recur in future in other data sets. This paper defines the recurrence of a rule, and aims to find a criteria to distinguish between high recurrent rules and low recurrent ones using a data set for software defect prediction. An experiment with the Eclipse Mylyn defect data set showed that rules of lower than 30 transactions showed low recurrence. We also found that the lower bound of transactions to select high recurrence rules is dependent on the required precision of defect prediction.

本文言語英語
ホスト出版物のタイトル2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509008063
DOI
出版ステータス出版済み - 8 23 2016
イベント15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 - Okayama, 日本
継続期間: 6 26 20166 29 2016

その他

その他15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016
国/地域日本
CityOkayama
Period6/26/166/29/16

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • エネルギー工学および電力技術
  • 制御と最適化

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