Iterated feature selection algorithms with layered recurrent neural network for software fault prediction
作者：
Highlights：
• Fault prediction improves the effectiveness of software quality assurance activities.
• This paper focuses on building an effective fault prediction classifier.
• Fault prediction model using Iterated feature selection algorithms with L-RNN.
• We perform experiments on 19 open source projects.
• Fault prediction model is best suitable for projects with faulty classes less than the threshold value.
摘要
•Fault prediction improves the effectiveness of software quality assurance activities.•This paper focuses on building an effective fault prediction classifier.•Fault prediction model using Iterated feature selection algorithms with L-RNN.•We perform experiments on 19 open source projects.•Fault prediction model is best suitable for projects with faulty classes less than the threshold value.
论文关键词：Software fault prediction,Feature selection,Layered recurrent neural network
论文评审过程：Received 3 February 2018, Revised 18 December 2018, Accepted 19 December 2018, Available online 25 December 2018, Version of Record 28 December 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.12.033
