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Performance Analysis of Software Aging Prediction

Volume 14, Number 11, November 2018, pp. 2692-2701
DOI: 10.23940/ijpe.18.11.p15.26922701

Yongquan Yan

School of Statistics, Shanxi University of Finance and Economics, Taiyuan, 030006, China

(Submitted on August 11, 2018; Revised on September 7, 2018; Accepted on October 15, 2018)


Software aging is a problem that was discovered two decades ago. Since then, many research studies have investigated how to manage aging problems caused by memory leakage and accumulated round-off error through resource consumption prediction or state forecasting. When applying state prediction, the performances of various aging classification algorithms are compared by the prediction error. Since forecasting error is not a precise measure and must be estimated, the forecast error variance needs to be analyzed. In this work, we carefully analyze the forecast error variance by three steps. In the first step, we propose a method to decompose the variance by considering the influence of the data sampling process and data partition procedure. In the second step, we use an enhanced Friedman test and the Nemenyi post hoc test to analyze the influence of the data sampling process on the data partitioning procedure. In the last step, a corrected t-test is proposed to compare the performance of two off-the-shelf classification algorithms. The software comparison experiment is based on a real-time web environment. We end this work by proposing a set of feasible suggestions.


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