Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM)

In recent years, the software industry has invested substantial effort to improve software quality in organizations. Applying proactive software defect prediction will help developers and white box testers to find the defects earlier, and this will reduce the time and effort. Traditional software de...

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Auteurs principaux:	Ahmed Bahaa Farid, Enas Mohamed Fathy, Ahmed Sharaf Eldin, Laila A. Abd-Elmegid
Format:	article
Langue:	EN
Publié:	PeerJ Inc. 2021
Sujets:	Defect Software defect prediction Abstract syntax tree Machine learning Deep learning Convolutional neural network Electronic computers. Computer science QA75.5-76.95
Accès en ligne:	https://doaj.org/article/2c247fe8e5db4cf1a34df6acf36a648e
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Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM)

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