Title

Evolutionary test data generation: A comparison of fitness functions.

SelectedWorks Author Profiles:

Alison L. Watkins

Document Type

Article

Publication Date

2006

ISSN

0038-0644

Abstract

Previous research using genetic algorithms to automate the generation of data for path testing has utilized several different fitness functions, assessing their usefulness by comparing them to random generation. This paper describes two sets of experiments that assess the performance of several fitness functions, relative to one another and to random generation. The results demonstrate that some fitness functions provide better results than others, generating fewer test cases to exercise a given program path. In these studies, the branch predicate and inverse path probability approaches were the best performers, suggesting that a two-step process combining these two methods may be the most efficient and effective approach to path testing.

Comments

Citation only. Full-text article is available through licensed access provided by the publisher. Members of the USF System may access the full-text of the article through the authenticated link provided.

Language

en_US

Publisher

John Wiley & Sons Ltd.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.