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Existing test techniques focus on particular, relevant aspects of the requirements of the system under test (SUT). Real-life SUTs have, however, numerous features to simultaneously be considered, often leading to a large number of tests. In such cases, because of time and cost constraints the entire set of tests cannot be run. It is then essential to prioritize the tests

Man-machine systems have many features that are to be considered simultaneously. Their validation often leads to a large number of tests; due to time and cost constraints they cannot exhaustively be run. It is then essential to prioritize the test subsets in accordance with their importance for relevant features. This paper applies soft-computing techniques to the prioritizing problem and proposes a graph model-based approach where preference degrees are indirectly determined. Events, which imply the relevant system behavior, are classified, and test cases are clustered using (i) unsupervised neural network clustering, and (ii) Fuzzy c-Means clustering algorithm. Two industrial case studies validate the approach and compare the applied techniques.

This chapter proposes a graph-model-based approach to prioritizing the test process. Tests are ranked according to their preference degrees which are determined indirectly, i.e., through classifying the events. For construction of the groups of events, an unsupervised neural network is trained by adaptive competitive learning algorithm. A case study demonstrates and validates the approach.

Model-based testing is related to particular, relevant features of the software under test (SUT) and its environment. Real-life systems often require a large number of tests, 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, which are usually given by several attributes of relevant events entailed. Based on event-oriented graph models, this paper proposes an approach to ranking test cases in accordance with their preference degrees. For forming preference groups, events are clustered using unsupervised neural network and fuzzy c-means clustering algorithm. The suggested approach is model-based, so that it does not necessitate the availability of the source code of the SUT. It differs from the existing approaches also in that it needs no prior information about the tests carried out before. Thus, it can be used to reflect tester's preferences not only for regression testing as is common in the literature but also for ranking test cases in any stage of software development. For the purpose of experimental evaluation, we compare the suggested prioritization approach with the six well-known prioritization methods.

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.