Abstract
Graph-based algorithms are commonly used to automatically gener ate test cases for coverage-oriented testing of software systems. Because of time and cost constraints, the entire set of test cases generated by those algorithms cannot be run. It is then essential to prioritize the test cases in sense of a rank ing, i.e., to order them according to their significance which usually is given by several attributes of relevant events entailed. This paper suggests unsupervised neural network clustering of test cases for forming preference groups, where adaptive competitive learning algorithm is applied for training the neural net work used. A case study demonstrates and validates the approach.
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Gökçe, N., Eminov, M., Belli, F. (2006). Coverage-Based, Prioritized Testing Using Neural Network Clustering. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds) Computer and Information Sciences – ISCIS 2006. ISCIS 2006. Lecture Notes in Computer Science, vol 4263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11902140_110
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DOI: https://doi.org/10.1007/11902140_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47242-1
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