Improved Genetic Programming Model for Software Reliability

被引：2

作者：

Cheng, Huifang ^{[1
]}

Zhang, Yongqiang ^{[1
]}

Zhao, Jing ^{[2
]}

机构：

[1] Hebei Univ Engn, Sch Informat & Elect Engn, Handan, Peoples R China

[2] Lib Handan Coll, Handan, Peoples R China

来源：

2009 INTERNATIONAL ASIA SYMPOSIUM ON INTELLIGENT INTERACTION AND AFFECTIVE COMPUTING | 2009年

关键词：

Genetic Programming; convergence; software reliability model; Orthogonal experimental design;

D O I：

10.1109/ASIA.2009.38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many existing software reliability models are based on some subjective assumptions those could be easily impractical in reality. Genetic Programming(GP for short) does not need some subjective assumption due to the basic characteristic of the data. Also, this method doesn't require to understand the inherent processes for failures, but to create models based on the given data for a "true" process during the specific modeling course, which can describe the software failure mechanisms more effectually and predict for the next failure times more exactly. This paper adopts improved GP(IGP for short) algorithm to hunting model, which can possibly reflect system behaviors, in the function spaces are compoundly constituted by the authorized function operators. Meanwhile, we have proved that IGP can obtain the best solution for failure behavior's variation rules from the convergence character of itself. Moreover, this paper makes use of Orthogonal experimental to adjust the parameters.

引用

页码：164 / +

页数：2