Variance analysis of software ageing problems. Issue 1 (1st February 2018)

• Record Type: Journal Article
• Authors: Yan, Yongquan
• Is Part Of: IET software. Volume 12:Issue 1(2018)
• Page Start: 41
• Page End: 48
• Publication Date: 2018-02-01
• Subjects: software metrics -- data handling -- regression analysis -- neural nets -- autoregressive moving average processes; variance change analysis -- software ageing problems -- resource consumption prediction problems -- variance decomposition -- data partitioning process -- regression algorithms -- autoregressive integrated moving average -- artificial neuron network -- forecasted estimation loss -- data sampling procedure; Computer software -- Periodicals; Software engineering -- Periodicals; 005.1
• DOI: 10.1049/iet-sen.2016.0290
• Languages: English
• ISSNs: 1751-8806

Abstract

Abstract : Software ageing problems are mainly caused by resource consumption exhaustion, so many researchers focused on predicting software resource consumption. However, the loss analysis using variance has not been done. In this study, the authors propose a framework to analyse variance change in the resource consumption prediction problems. This framework is made up of three steps. First, an original variance decomposition is proposed in view of data sampling and partitioning process. Second, in order to study the influence of data sampling and partitioning process to the variance, the enhanced Friedman test plus Nemenyi post‐hoc test is introduced. Lastly, they propose a corrected t ‐test to analyse the performances of two regression algorithms: auto‐regressive integrated moving average and artificial neuron network. In the experiments, they analyse the variance in two levels: operating system level and application level. They find the result that k is equal to ten for k ‐fold cross‐validation is proper for resource consumption prediction, although the contribution to variance is almost same for the sensitivity of forecasted estimation loss in consideration of data partitioning process and the sensitivity of forecasted estimation loss in consideration of data sampling procedure.