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http://dx.doi.org/10.7236/JIIBC.2022.22.6.113

Prediction of Software Fault Severity using Deep Learning Methods  

Hong, Euyseok (Dept. of Computer Engineering, Sungshin Women's University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.22, no.6, 2022 , pp. 113-119 More about this Journal
Abstract
In software fault prediction, a multi classification model that predicts the fault severity category of a module can be much more useful than a binary classification model that simply predicts the presence or absence of faults. A small number of severity-based fault prediction models have been proposed, but no classifier using deep learning techniques has been proposed. In this paper, we construct MLP models with 3 or 5 hidden layers, and they have a structure with a fixed or variable number of hidden layer nodes. As a result of the model evaluation experiment, MLP-based deep learning models shows significantly better performance in both Accuracy and AUC than MLPs, which showed the best performance among models that did not use deep learning. In particular, the model structure with 3 hidden layers, 32 batch size, and 64 nodes shows the best performance.
Keywords
Software fault severity; Quality prediction; Deep learning;
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Times Cited By KSCI : 4  (Citation Analysis)
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