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 …

Defect prediction is one of the key challenges in software development and programming
language research for improving software quality and reliability. The problem in this area is …

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

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

Software defects not only reduce operational reliability but also significantly increase overall
maintenance costs. Consequently, it is necessary to predict software defects at an early …

Software fault prediction (SFP) aims to improve software quality with a possible minimum
cost and time. Various machine learning models have been proposed in the past for …

Context: Most defect prediction methods consider a series of traditional manually designed
static code metrics. However, only using these hand-crafted features is impractical. Some …

Software defect prediction recommends the most defect-prone software modules for
optimization of the test resource allocation. The limitation of the extensively-studied …

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 …

The popularity of mobile devices has led to an explosive growth in the number of mobile
apps in which Android mobile apps are the mainstream. Android mobile apps usually …