Improving regression testing performance using the Adaptive Resonance Theory-2A self-organising neural network architecture
by Adenilso S. Simao, Rodrigo F. De Mello, Luciano J. Senger, Laurence T. Yang
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 1, No. 3, 2008

Abstract: Regression testing applies a previously developed test case suite to new software versions. A traditional approach is the execution of all test cases, although, this may be time consuming and, sometimes, not necessary as the source code modification may affect only a test case subset. Some initiatives have addressed this issue. For instance, one of the most promising ones is the modified-based technique that selects test cases based on whether they execute the modified parts of the program. This technique is conservative, but it often selects test cases that are not relevant. This article presents an approach to select test case subsets by using an Adaptive Resonance Theory-2A self-organising neural network architecture. In this approach, test cases are summarised in feature vectors with code coverage information, which are classified by the neural network in clusters. Clusters are labelled representing each software functionality evaluated by the coverage criterion. A new software version is then analysed to determine modified points and, then, clusters, which represent the related functionalities, are chosen. The test case subset is obtained from these clusters. Experiments were conducted to evaluate the approach using feature vectors based on all-uses and -nodes code coverage information against a modification-based technique.

Online publication date: Sun, 03-Aug-2008

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 Autonomous and Adaptive Communications Systems (IJAACS):
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