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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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第一作者： Ahmed Bahaa,Farid

第一单位： Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt.;Department of Information Systems, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.

作者： Ahmed Bahaa,Farid ^[1] ; Enas Mohamed,Fathy ^[2] ; Ahmed,Sharaf Eldin ^[3] ; Laila A,Abd-Elmegid ^[2]

作者单位： Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt.;Department of Information Systems, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt. ^[1] Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt. ^[2] Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt.;Department of Information Systems, Faculty of Information Technology and Computer Science, Sinai University, Sinai, Egypt. ^[3]

关键词 Abstract syntax tree Bidirectional long short-term memory Convolutional neural network Deep learning Machine learning Software defect prediction Defect

DOI 10.7717/peerj-cs.739

PMID 34901421

发布时间 2021-12-15

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

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