Fuzzing, a widely-used technique for bug detection, has seen advancements through Large
Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing …

Fuzzing has proven to be a highly effective approach to uncover software bugs over the past
decade. After AFL popularized the groundbreaking concept of lightweight coverage …

Database management systems (DBMSs) are critical components within software
ecosystems, and their security and stability are paramount. In recent years, fuzzing has …

Database management systems (DBMSs) are crucial for storing and fetching data. To
improve the reliability of such systems, approaches have been proposed to detect logic bugs …

Automated test generation can help developers craft high-quality software tests while
mitigating the manual effort needed for writing test code. Despite significant research efforts …

Database Management Systems (DBMSs) provide efficient data retrieval and manipulation
for many applications through Structured Query Language (SQL). Incorrect implementations …

The SQL specification consists of hundreds of statement types, which leads to difficulties in
DBMS fuzzing: state-of-the-art works generally reuse the statements of predefined types; the …

Tensor compilers are essential for deploying deep learning applications across various
hardware platforms. While powerful, they are inherently complex and present significant …

Differential testing is a prevalent strategy for establishing test oracles in automated DBMS
testing. However, meticulously selecting equivalent DBMSs with diverse implementations …

Quantum chemistry software implements the first principle quantum computation and is
indispensable in both scientific research and chemical industries. Any bugs in such software …