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access icon free Variance analysis of software ageing problems

© The Institution of Engineering and Technology
Received 26/11/2016, Accepted 27/05/2017, Revised 08/05/2017, Published 01/06/2017

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.

Inspec keywords: software metrics; data handling; autoregressive moving average processes; regression analysis; neural nets

Other keywords: artificial neuron network; data partitioning process; resource consumption prediction problems; software ageing problems; variance decomposition; autoregressive integrated moving average; regression algorithms; data sampling procedure; variance change analysis; forecasted estimation loss

Subjects: Neural computing techniques; Other topics in statistics; Data handling techniques; Software metrics

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