DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode

Deep Neural Embedding for Software Vulnerability Discovery: Comparison and Optimization

(2022) Xue Yuan et al.   Security and Communication Networks

AndroAnalyzer: android malicious software detection based on deep learning

(2021) Recep Sinan Arslan   PeerJ Computer Science

An Empirical Study on Software Defect Prediction Using CodeBERT Model

(2021) Cong Pan et al.   Applied Sciences-Basel

A Survey of Android Malware Detection with Deep Neural Models

(2020) Junyang Qiu et al.   ACM COMPUTING SURVEYS

FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks

(2020) Wei Hua et al.   IEEE TRANSACTIONS ON RELIABILITY

Just-In-Time Defect Identification and Localization: A Two-Phase Framework

(2020) Meng Yan et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

FineLocator: A novel approach to method-level fine-grained bug localization by query expansion

(2019) Wen Zhang et al.   INFORMATION AND SOFTWARE TECHNOLOGY

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

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

MalDozer: Automatic framework for android malware detection using deep learning

(2018) ElMouatez Billah Karbab et al.   Digital Investigation

The impact of IR-based classifier configuration on the performance and the effort of method-level bug localization

(2018) Chakkrit Tantithamthavorn et al.   INFORMATION AND SOFTWARE TECHNOLOGY

Detecting Android malware using Long Short-term Memory (LSTM)

(2018) R. Vinayakumar et al.   JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

A Survey of Machine Learning for Big Code and Naturalness

(2018) Miltiadis Allamanis et al.   ACM COMPUTING SURVEYS

Back-propagation neural network on Markov chains from system call sequences: a new approach for detecting Android malware with system call sequences

(2017) Xi Xiao et al.   IET Information Security

On Locating Malicious Code in Piggybacked Android Apps

(2017) Li Li et al.   JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

Defect Prediction in Android Binary Executables Using Deep Neural Network

(2017) Feng Dong et al.   WIRELESS PERSONAL COMMUNICATIONS

An empirical framework for defect prediction using machine learning techniques with Android software

(2016) Ruchika Malhotra   APPLIED SOFT COMPUTING

Identifying Android malware with system call co-occurrence matrices

(2016) Xi Xiao et al.   Transactions on Emerging Telecommunications Technologies

Empirical assessment of machine learning-based malware detectors for Android

(2014) Kevin Allix et al.   EMPIRICAL SOFTWARE ENGINEERING

Predicting Vulnerable Software Components via Text Mining

(2014) Riccardo Scandariato et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

DConfusion: a technique to allow cross study performance evaluation of fault prediction studies

(2013) David Bowes et al.   Automated Software Engineering