By Topic

Neural-Network Based Test Cases Generation Using Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Zhao, R. ; Beijing Univ. of Chem. Technol., Beijing ; Shanshan Lv

A key issue in black-box testing is how to select adequate test cases from input domain on the basis of specification. However, for some kinds of software, developing test cases from output domain is more suitable than from input domain. In this paper, we present a novel approach to automatically generate test cases from output domain. A model is created via neural network to take as a function substitute for the software under test, and then on the basis of the created function model, for given outputs we employ an improved genetic algorithm to find the corresponding inputs, so that the automation of test cases generation from output domain is completed. In order to investigate the effectiveness of the approach, a number of experiments have been conducted on two different software programs under test. Experimental results show that this approach is promising and effective.

Published in:

Dependable Computing, 2007. PRDC 2007. 13th Pacific Rim International Symposium on

Date of Conference:

17-19 Dec. 2007