Software reliability forecasting by support vector machines with simulated annealing algorithms

被引：121
|
作者：
Pai, Ping-Feng
Hong, Wei-Chiang
机构：
[1] Natl Chi Nan Univ, Dept Informat Management, Puli 545, Nantou, Taiwan
[2] Da Yeh Univ, Sch Management, Da Tusen 51505, Chang Hua, Taiwan
来源：
JOURNAL OF SYSTEMS AND SOFTWARE | 2006年 / 79卷 / 06期
关键词：
support vector machines; simulated annealing algorithms; software reliability forecasting;
D O I：
10.1016/j.jss.2005.02.025
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Support vector machines (SVMs) have been successfully employed to solve non-linear regression and time series problems. However, SVMs have rarely been applied to forecasting software reliability. This investigation elucidates the feasibility of the use of SVMs to forecast software reliability. Simulated annealing algorithms (SA) are used to select the parameters of an SVM model. Numerical examples taken from the existing literature are used to demonstrate the performance of software reliability forecasting. The experimental results reveal that the SVM model with simulated annealing algorithms (SVMSA) results in better predictions than the other methods. Hence, the proposed model is a valid and promising alternative for forecasting software reliability. (C) 2005 Elsevier Inc. All rights reserved.
引用
收藏
页码：747 / 755
页数：9
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