Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It
generates a large number of test cases and monitors the executions for defects. Fuzzing has …

Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and
eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to …

This paper provides a survey of the emerging area of Large Language Models (LLMs) for
Software Engineering (SE). It also sets out open research challenges for the application of …

Large language models (large LMs) are increasingly trained on massive codebases and
used to generate code. However, LMs lack awareness of security and are found to …

Large Language Models (LLMs) such as OpenAI Codex are increasingly being used as AI-
based coding assistants. Understanding the impact of these tools on developers' code is …

Unlike traditional software, smart contracts have the unique organization in which a
sequence of transactions shares persistent states. Unfortunately, such a characteristic …

Internet of things (IoT) devices make up 30% of all network-connected endpoints,
introducing vulnerabilities and novel attacks that make many companies as primary targets …

High scalability and low running costs have made fuzz testing the de facto standard for
discovering software bugs. Fuzzing techniques are constantly being improved in a race to …

The release of AFL marked an important milestone in the area of software security testing,
revitalizing fuzzing as a major research topic and spurring a large number of research …

Coverage-based greybox fuzzing (CGF) is one of the most successful approaches for
automated vulnerability detection. Given a seed file (as a sequence of bits), a CGF randomly …