Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

被引：32
|
作者：
Chatterjee, S. [1 ]
Nigam, S. [1 ]
Singh, J. B. [1 ]
Upadhyaya, L. N. [1 ]
机构：
[1] Indian Sch Mines, Dept Appl Math, Dhanbad 826004, Bihar, India
来源：
APPLIED INTELLIGENCE | 2012年 / 37卷 / 01期
关键词：
Software reliability; NARX neural network; Faults; Time between failures; STEP-AHEAD PREDICTION; NEURAL-NETWORKS; SYSTEM-IDENTIFICATION; RELIABILITY;
D O I：
10.1007/s10489-011-0316-x
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.
引用
收藏
页码：121 / 129
页数：9
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