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 …

Transformers have gained popularity in the software engineering (SE) literature. These deep
learning models are usually pre-trained through a self-supervised objective, meant to …

Over the past decade, Open Source Software (OSS) has experienced rapid growth and
widespread adoption, attributed to its openness and editability. However, this expansion has …

Automated program repair (APR) faces the challenge of test overfitting, where generated
patches pass validation tests but fail to generalize. Existing methods for patch assessment …

With the rapid increasing number of open source software (OSS), the majority of the software
vulnerabilities in the open source components are fixed silently, which leads to the deployed …

Representing code changes as numeric feature vectors, ie, code change representations, is
usually an essential step to automate many software engineering tasks related to code …

Learning-based techniques, especially advanced Large Language Models (LLMs) for code,
have gained considerable popularity in various software engineering (SE) tasks. However …

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 …

Identifying vulnerabilities in software code is crucial for ensuring the security of modern
systems. However, manual detection requires expert knowledge and is time-consuming …

Open-source software (OSS) vulnerabilities are increasingly prevalent, emphasizing the
importance of security patches. However, in widely used security platforms like NVD, a …