A Technique to Reduce the Test Case Suites for Regression Testing Based on a Self-Organizing Neural Network Architecture

This paper presents a technique to select subsets of the test cases, reducing the time consumed during the evaluation of a new software version and maintaining the ability to detect defects introduced. Our technique is based on a model to classify test case suites by using an ART-2A self-organizing neural network architecture. Each test case is summarized in a feature vector, which contains all the relevant information about the software behavior. The neural network classifies feature vectors into clusters, which are labeled according to software behavior. The source code of a new software version is analyzed to determine the most adequate clusters from which the test case subset will be selected. Experiments compared feature vectors obtained from all-uses code coverage information to a random selection approach. Results confirm the new technique has improved the precision and recall metrics adopted