Title: Improving regression testing performance using the Adaptive Resonance Theory-2A self-organising neural network architecture

Authors: Adenilso S. Simao, Rodrigo F. De Mello, Luciano J. Senger, Laurence T. Yang

Addresses: Universidade de Sao Paulo, Instituto de Ciencias Matematicas e de Computacao, 13560 970 Sao Carlos, Brazil. ' Universidade de Sao Paulo, Instituto de Ciencias Matematicas e de Computacao, 13560 970 Sao Carlos, Brazil. ' Departamento de Informatica, Universidade Estadual de Ponta Grossa, 84032 340 Ponta Grossa, Brazil. ' Departament of Computer Science, St. Francis Xavier University, Antigonish B2G 2W5, Canada

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.

Keywords: adaptive resonance theory; ART; neural networks; regression testing; test classification; new software versions; clustering.

DOI: 10.1504/IJAACS.2008.019811

International Journal of Autonomous and Adaptive Communications Systems, 2008 Vol.1 No.3, pp.370 - 385

Published online: 03 Aug 2008 *

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