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 …

The detection of software vulnerability requires critical attention during the development
phase to make it secure and less vulnerable. Vulnerable software always invites hackers to …

Memory-related vulnerabilities constitute severe threats to the security of modern software.
Despite the success of deep learning-based approaches to generic vulnerability detection …

Data-driven research on the automated discovery and repair of security vulnerabilities in
source code requires comprehensive datasets of real-life vulnerable code and their fixes. To …

Software quality assurance efforts often focus on identifying defective code. To find likely
defective code early, change-level defect prediction-aka. Just-In-Time (JIT) defect prediction …

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

Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …

Defect prediction models are proposed to help a team prioritize the areas of source code
files that need Software Quality Assurance (SQA) based on the likelihood of having defects …

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 …

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