A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method

Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset

(2022) Elsie Fezeka Swana et al.   SENSORS

Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges

(2022) Abigail M. Y. Koay et al.   JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

Attention based GRU-LSTM for software defect prediction

(2021) Hafiz Shahbaz Munir et al.   PLoS One

Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction

(2021) Shuo Feng et al.   INFORMATION AND SOFTWARE TECHNOLOGY

Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

(2021) Shivani Jain et al.   SCIENCE OF COMPUTER PROGRAMMING

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

(2021) Ahmed Bahaa Farid et al.   PeerJ Computer Science

Software defect prediction via LSTM

(2020) Jiehan Deng et al.   IET Software

Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network

(2020) Kun Zhu et al.   IET Software

Software Defect Prediction via Attention-Based Recurrent Neural Network

(2019) Guisheng Fan et al.   Scientific Programming

Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals

(2019) Xueyi Li et al.   Applied Sciences-Basel

An Improved CNN Model for Within-Project Software Defect Prediction

(2019) Pan et al.   Applied Sciences-Basel

A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

(2019) Ashima Kukkar et al.   SENSORS

Transfer Convolutional Neural Network for Cross-Project Defect Prediction

(2019) Shaojian Qiu et al.   Applied Sciences-Basel

Software defect prediction via cost-sensitive Siamese parallel fully-connected neural networks

(2019) Linchang Zhao et al.   NEUROCOMPUTING

BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques

(2019) Sushant Kumar Pandey et al.   EXPERT SYSTEMS WITH APPLICATIONS

Deep learning based software defect prediction

(2019) Lei Qiao et al.   NEUROCOMPUTING

SLDeep: Statement-level software defect prediction using deep-learning model on static code features

(2019) Amirabbas Majd et al.   EXPERT SYSTEMS WITH APPLICATIONS

Progress on approaches to software defect prediction

(2018) Zhiqiang Li et al.   IET Software

Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning

(2018) Haonan Tong et al.   INFORMATION AND SOFTWARE TECHNOLOGY

A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks

(2018) Diana-Lucia Miholca et al.   INFORMATION SCIENCES

Automatically identifying code features for software defect prediction: Using AST N-grams

(2018) Thomas Shippey et al.   INFORMATION AND SOFTWARE TECHNOLOGY

Which type of metrics are useful to deal with class imbalance in software defect prediction?

(2017) Muhammed Maruf Öztürk   INFORMATION AND SOFTWARE TECHNOLOGY

Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data

(2017) Mateusz Lango et al.   JOURNAL OF INTELLIGENT INFORMATION SYSTEMS

HYDRA: Massively Compositional Model for Cross-Project Defect Prediction

(2016) Xin Xia et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Negative samples reduction in cross-company software defects prediction

(2015) Lin Chen et al.   INFORMATION AND SOFTWARE TECHNOLOGY

A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

(2012) Andrea Arcuri et al.   SOFTWARE TESTING VERIFICATION & RELIABILITY

BRACID: a comprehensive approach to learning rules from imbalanced data

(2011) Krystyna Napierala et al.   JOURNAL OF INTELLIGENT INFORMATION SYSTEMS