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.

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

“…The issue here is, most of the algorithm nowadays could be tuned into different type ML technique. To make things clearer, work by [105] using neural network algorithm in classification technique, while work by [103] tweak the neural network to work on clustering technique. Therefore, to avoid misleading information, authors agreed to not list out algorithms available for each ML technique category as the algorithm can be tweak to fit the technique intended.…”

On the Applicability of Soft Computing Techniques in Regression Testing

“…The work proposed by Gokce, N., Eminli, M. uses multilayer perceptron neural network in order to prioritize the test cases using model based prioritization [17]. The work was implemented on 100 test cases, which were derived from ESG of web based software system.…”