BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques
作者：
Highlights：
• Proposed model is for software bug prediction to reduce testing effort.
• Proposed model also address to the problem of class imbalance and over fitting problem.
• Experiments conducted on Promise repository NASA dataset.
• F-measure, ROC, MCC and PRC used as performance metrics.
• Performance of model is high amongst existing methods.
摘要
•Proposed model is for software bug prediction to reduce testing effort.•Proposed model also address to the problem of class imbalance and over fitting problem.•Experiments conducted on Promise repository NASA dataset.•F-measure, ROC, MCC and PRC used as performance metrics.•Performance of model is high amongst existing methods.
论文关键词：Software bug prediction,Classification technique,Software metrics,Deep representation,Boosting,Staked denoising auto-encoder,Heterogeneous Ensemble learning technique
论文评审过程：Received 21 December 2018, Revised 18 July 2019, Accepted 10 November 2019, Available online 15 November 2019, Version of Record 21 November 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113085
