Revolutionizing Software Defect Prediction Through Deep Learning

This study aims to revolutionize software defect prediction by leveraging deep learning (DL) techniques, specifically focusing on Convolutional Neural Networks (CNN) and Stack Sparse Autoencoders (SSAE). The research involves training these models on datasets from the NASA Metrics Data Program, usin...

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Bibliographic Details

Published in:	2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT) Vol. 1; pp. 438 - 442
Main Authors:	G, Selvin Jose, Charles, J
Format:	Conference Proceeding
Language:	English
Published:	IEEE 08.08.2024
Subjects:	Accuracy Convolutional Neural Network Deep learning NASA Neural networks Software Defect Prediction Software Engineering Software quality Stacked Sparse Autoencoder Training Transforms
Online Access:	Get full text
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