By Topic

Optimal Software Testing Case Design Based on Self-Learning Control 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

1 Author(s)
Huang Ying Lulu ; Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China

This paper demonstrates an approach to optimizing software testing cases by rapidly fixing software deficiency with given software parameter uncertainty during a regressive testing process. Taking the software testing process into a time-varied system control problem, a state transform matrix model is presented. Because regressive testing is an iterative process, the two-dimensional variable-factor self-learning strategy is used to optimize the test case. The simulation results show that the learning control strategy is better than either random testing or the Markov testing strategy, and it can significantly reduce regressive test numbers and save test costs.

Published in:

Parallel and Distributed Processing with Applications (ISPA), 2010 International Symposium on

Date of Conference:

6-9 Sept. 2010