In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

An increasingly popular set of techniques adopted by software engineering (SE)
researchers to automate development tasks are those rooted in the concept of Deep …

Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …

The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …

Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

Detecting semantically similar binary functions–a crucial capability with broad security
usages including vulnerability detection, malware analysis, and forensics–requires …

One truism of deep learning is that the automatic feature engineering (seen in the first layers
of those networks) excuses data scientists from performing tedious manual feature …

Predictive models are one of the most important techniques that are widely applied in many
areas of software engineering. There have been a large number of primary studies that …

Background: Just-In-Time (JIT) defect prediction models predict if a commit will introduce
defects in the future. DeepJIT and CC2Vec are two state-of-the-art JIT Deep Learning (DL) …

Cross-project defect prediction (CPDP) refers to predicting defects in the target project
lacking of defect data by using prediction models trained on the historical defect data of …