Cargando…
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...
Autores principales: | Farid, Ahmed Bahaa, Fathy, Enas Mohamed, Sharaf Eldin, Ahmed, Abd-Elmegid, Laila A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627227/ https://www.ncbi.nlm.nih.gov/pubmed/34901421 http://dx.doi.org/10.7717/peerj-cs.739 |
Ejemplares similares
-
Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model
por: Jiang, Xuchu, et al.
Publicado: (2022) -
Detecting racism and xenophobia using deep learning models on Twitter data: CNN, LSTM and BERT
por: Benítez-Andrades, José Alberto, et al.
Publicado: (2022) -
Deepfake tweets classification using stacked Bi-LSTM and words embedding
por: Rupapara, Vaibhav, et al.
Publicado: (2021) -
Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)
por: Shambour, Mohd Khaled
Publicado: (2022) -
LSTM-based sentiment analysis for stock price forecast
por: Ko, Ching-Ru, et al.
Publicado: (2021)