How do we know a generated patch is correct? This is a key challenging question that
automated program repair (APR) systems struggle to address given the incompleteness of …

Random test case generation, or fuzzing, is a viable means for uncovering compiler bugs.
Unfortunately, compiler fuzzing can be time-consuming and inefficient with purely randomly …

As people pay more and more attention to software system security issues, fuzzy testing, as
a security testing technology for security vulnerability detection, has become more and more …

Background: Executing, verifying and enforcing credible transactions on permissionless
blockchains is done using smart contracts. A key challenge with smart contracts is ensuring …

The performance of GPU (Graphics Processing Unit)-accelerated functions affects a large
spectrum of modern software. Efficiently synchronizing across thousands of concurrent …

Using recorded online traffic for the regression testing of web applications has become a
common practice in industry. However, this" record and replay" on microservices is …

OpenCL programs typically employ complex storage models and diverse data types as well
as manifest various memory access patterns, which make it challenging to detect the …

Recently, the literature has proposed various heuristics to predict patch correctness. Csuvik
et al.[9] translate some empirical observations into a simple assumption for ranking valid …

State-of-the-art APR techniques currently produce patches that are manually evaluated as
overfitting, and these overfitting patches often worsen the original program, leading to …

随着人们对软件系统安全问题关注度的不断提升, 模糊测试作为一种用于安全漏洞检测的安全
测试技术, 具有自动化程度高, 误报率低等优点, 其应用越来越广泛, 地位也越来越重要 …