Model-Based Test Case Prioritization Using Neural Network Classification

Abstract

Model-based testing for real-life software systems often require a large number of tests, all of which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance the tester perceives. In this paper, this problem is solved by improving our given previous study, namely, applying classification approach to the results of our previous study functional relationship between the test case prioritization group membership and the two attributes: important index and frequency for all events belonging to given group are established. A for classification purpose, neural network (NN) that is the most advances is preferred and a data set obtained from our study for all test cases is classified using multilayer perceptron (MLP) NN. The classification results for commercial test prioritization application show the high classification accuracies about 96% and the acceptable test prioritization performances are achieved.