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

Yuanxun Shao, Bin Liu, Shihai Wang, Guoqi Li

Abstract: 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.