The testing of a system starts with the crafting of test cases. Not all the test cases are, however, equally important. The test cases can be prioritized using policies discussed in the work. The work proposes a neural network model to prioritize the test cases. The work has been validated using backpropagation neural network. 200 test cases were crafted and the experiment was carried out using 2, 5, 10, 15, and 20 layers neural network. The results have been reported and lead to the conclusion that neural network-based priority analyzer can predict the priority of a test.
CITATION STYLE
Bhasin, H., Khanna, E., & Sharma, K. (2016). Neural network-based automated priority assigner. In Advances in Intelligent Systems and Computing (Vol. 381, pp. 183–190). Springer Verlag. https://doi.org/10.1007/978-81-322-2526-3_20
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