SOFTWARE TESTING BY USING THE BLACK-BOX METHOD AND THE EQUIVALENCE PARTITION TECHNIQUE TO PREDICT THE ACCURACY OF THE NEURAL NETWORK BASE

  • zulkifli zulkifli universitas Asyah Pringsdwu
  • Ford Lumban Gaol
  • Harco Leslie Hendric Spits Warnars
  • Benfano Soewito
Keywords: Neural Network, Black-Box, Equivalence Partitions.

Abstract

A neural network algorithm is an artificial nervous system or artificial neural network, it is a physical cellular system that can acquire, store and use the knowledge gained from experience for activation using bipolar sigmoid where the output value ranges from -1 to 1. Because there is a yet of a neural network algorithm model to predict the level of accuracy in terms of software testing, the equivalent partitions black-box technique is used. The black-box software testing method is a testing approach where the data comes from defined functional requirements regardless of the final program structure, and the technique used is equivalent partitions. The design of the accurate prediction of this research is by determining the college application as the software to be tested, then testing it using the black-box method with the equivalence partitions technique. This test was chosen because it will find software errors in several categories. From black-box testing, a dataset is obtained to measure the accuracy of real output and predictive output. The last step is to calculate the error, RSME from the real output, and the predicted output. Furthermore, the final results of the research on the neural network algorithm that is applied to determine the prediction of the accuracy level of black-box software testing with the equivalent partitioning technique is the average accuracy above 80%.

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Published
2020-10-31
How to Cite
zulkifli, zulkifli, Gaol, F. L., Spits Warnars, H. L. H., & Soewito, B. (2020). SOFTWARE TESTING BY USING THE BLACK-BOX METHOD AND THE EQUIVALENCE PARTITION TECHNIQUE TO PREDICT THE ACCURACY OF THE NEURAL NETWORK BASE . COMPUSOFT: An International Journal of Advanced Computer Technology, 9(10), 3856-3859. Retrieved from https://ijact.in/index.php/ijact/article/view/1219