Defect prediction in software using spiderhunt-based deep convolutional neural network classifier
by M. Prashanthi; Chandra Mohan Miryala
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 27, No. 4, 2022

Abstract: In this research, the defects in the software are predicted using the deep CNN classifier by effectively optimising the classifier using spiderhunt optimisation. The effective communication and hunting characteristics of the spiderhunt are employed for tuning the classifier that boosts the classifier performance. The proposed spiderhunt optimisation not only optimises the classifier but also plays a significant role in the feature selection for the extraction of necessary features that helps in defect prediction. The proposed spiderhunt optimisation achieved an improvement rate of 1.009%, 1.083%, 0.578%, and 1.01% in terms of accuracy, precision, recall, and F-measure and is proved to be quite efficient compared to state of art methods.

Online publication date: Sun, 14-May-2023

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