A novel software defect prediction based on atomic class-association rule mining

作者：

Highlights：

• A new atomic class-association rule mining is built for software defect prediction.

• Redundant pruning is done using the relation between metrics in feature atomic rules.

• The algorithm is used to 15 commonly-available datasets from the MDP and PROMISE repository.

• The comparative experiments are performed in other well-known classifiers.

摘要

•A new atomic class-association rule mining is built for software defect prediction.•Redundant pruning is done using the relation between metrics in feature atomic rules.•The algorithm is used to 15 commonly-available datasets from the MDP and PROMISE repository.•The comparative experiments are performed in other well-known classifiers.

论文关键词：Software defect prediction,Data mining,Association rules,Apriori,Machine learning

论文评审过程：Received 5 March 2018, Revised 24 June 2018, Accepted 18 July 2018, Available online 20 July 2018, Version of Record 3 August 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.07.042