Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security
risks to many software systems. Given the limited resources in practice, SV assessment and …

Software Vulnerability Prediction (SVP) is a data-driven technique for software quality
assurance that has recently gained considerable attention in the Software Engineering …

The use of learning-based techniques to achieve automated software vulnerability detection
has been of longstanding interest within the software security domain. These data-driven …

Identifying security patches via code commits to allow early warnings and timely fixes for
Open Source Software (OSS) has received increasing attention. However, the existing …

Security vulnerabilities have been continually disclosed and documented. For the effective
understanding, management, and mitigation of the fast-growing number of vulnerabilities, an …

The lack of appropriate cyber security measures deployed on Internet of Things (IoT) makes
these devices prone to security issues. Consequently, the timely identification and detection …

Software composition analysis (SCA) tools are proposed to detect potential vulnerabilities
introduced by open-source software (OSS) imported as third-party libraries (TPL). With the …

Dynamic data-driven vulnerability assessments face massive heterogeneous data contained
in, and produced by SOCs (Security Operations Centres). Manual vulnerability assessment …

To avoid potential risks posed by vulnerabilities in third-party libraries, security researchers
maintain vulnerability databases (eg, NVD) containing vulnerability reports, each of which …

The Common Vulnerability Scoring System (CVSS) is a popular method for evaluating the
severity of vulnerabilities in vulnerability management. In the evaluation process, a numeric …