Driven by the increasing scale and high profile cybersecurity incidents related public data,
recent years we have witnessed a paradigm shift in understanding and defending against …

CONTEXT: Software metrics may be used in fault prediction models to improve software
quality by predicting fault location. OBJECTIVE: This paper aims to identify software metrics …

The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …

Context: Identifying potential vulnerable code is important to improve the security of our
software systems. However, the manual detection of software vulnerabilities requires expert …

This paper presents an approach based on machine learning to predict which components
of a software application contain security vulnerabilities. The approach is based on text …

Software vulnerabilities are the fundamental cause of many attacks. Even with rapid
vulnerability patching, the problem is more complicated than it looks. One reason is that …

Machine learning is now widely used to detect security vulnerabilities in the software, even
before the software is released. But its potential is often severely compromised at the early …

Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a
variety of problems including deadlock, hacking, information loss and system failure. A …

Grey-box fuzz testing has revealed thousands of vulnerabilities in real-world software owing
to its lightweight instrumentation, fast coverage feedback, and dynamic adjusting strategies …

Software vulnerabilities pose significant security risks to the host computing system. Faced
with continuous disclosure of software vulnerabilities, system administrators must prioritize …