Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems
作者：
Highlights：
• This paper expands the use of ensemble methods for the prediction of number of faults unlikely the earlier works on ensemble methods that focused on predicting software modules as faulty or non-faulty.
• This paper investigates the usage of both heterogeneous ensemble methods as well as homogeneous ensemble methods for the prediction of number of faults.
• We present two linear combination rules and two non-linear combination rules for combining the outputs of the base learners in the ensemble.
• In addition, we assess the performance of ensemble methods under two different scenarios, intra-release prediction and inter-releases prediction.
• The experiments are performed over five open-source software systems with their fifteen releases, collected from the PROMISE data repository.
摘要
•This paper expands the use of ensemble methods for the prediction of number of faults unlikely the earlier works on ensemble methods that focused on predicting software modules as faulty or non-faulty.•This paper investigates the usage of both heterogeneous ensemble methods as well as homogeneous ensemble methods for the prediction of number of faults.•We present two linear combination rules and two non-linear combination rules for combining the outputs of the base learners in the ensemble.•In addition, we assess the performance of ensemble methods under two different scenarios, intra-release prediction and inter-releases prediction.•The experiments are performed over five open-source software systems with their fifteen releases, collected from the PROMISE data repository.
论文关键词：Software fault prediction,Ensemble methods,Heterogeneous ensemble,Prediction of number of faults
论文评审过程：Received 4 August 2016, Revised 25 October 2016, Accepted 18 December 2016, Available online 21 December 2016, Version of Record 25 January 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2016.12.017
