Applying genetic algorithm to optimise the software testing efficiency with Euclidean distance
by Rijwan Khan; Mohd Amjad
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 23, No. 1/2, 2022

Abstract: Software testing ensures that the developed software is error free and reliable for customer use. For verification and validation of software products, testing has been applied to these products in various software industries. So, before the delivery of the software to the customer, all the types of testing have been applied. In this paper, automatic test cases have been developed with the help of a genetic algorithm for data flow testing and these tests are divided into different groups using Euclidean distance. Elements of each group are applied to the data flow diagram of the program/software and all the du-paths are found, covering the given test suits. New test suits are generated with the help of the genetic algorithm to cover all du-paths.

Online publication date: Mon, 05-Sep-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com