Adaptive random testing with CG constraint

被引：12

作者：

Chan, FT ^{[1
]}

Chan, KP ^{[1
]}

Chen, TY ^{[1
]}

Yiu, SM ^{[1
]}

机构：

[1] Univ Hong Kong, Sch Profess & Continuing Educ, Hong Kong, Hong Kong, Peoples R China

来源：

PROCEEDINGS OF THE 28TH ANNUAL INTERNATIONAL COMPUTER SOFTWARE AND APPLICATION CONFERENCE, WORKSHOP AND FAST ABSTRACTS | 2004年

关键词：

random testing; Adaptive Random Testing; center of gravity constraint;

D O I：

10.1109/ISIC.2004.1387665

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce a C. G. constraint on Adaptive Random Testing (ART) for programs with numerical input. One rationale behind Adaptive Random Testing is to have the test candidates to be as widespread over the input domain as possible. However, the computation may be quite expensive in some cases. The C. G. constraint is introduced to maintain the widespreadness while reducing the computation requirement in terms of number of distance measures. Three variations of C. G. constraints and their performance when compared with ART are discussed.

引用

页码：96 / 99

页数：4