Code intelligence leverages machine learning techniques to extract knowledge from
extensive code corpora, with the aim of develo** intelligent tools to improve the quality …

Machine learning (ML) techniques increase the effectiveness of software engineering (SE)
lifecycle activities. We systematically collected, quality-assessed, summarized, and …

Static bug detection has shown its effectiveness in detecting well-defined memory errors, eg,
memory leaks, buffer overflows, and null dereference. However, modern software systems …

While static analysis is instrumental in uncovering software bugs, its precision in analyzing
large and intricate codebases remains challenging. The emerging prowess of Large …

Machine learning and its promising branch deep learning have shown success in a wide
range of application domains. Recently, much effort has been expended on applying deep …

With the increase in software vulnerabilities that cause significant economic and social
losses, automatic vulnerability detection has become essential in software development and …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

Hybrid testing combines fuzz testing and concolic execution. It leverages fuzz testing to test
easy-to-reach code regions and uses concolic execution to explore code blocks guarded by …

Code retrieval techniques and tools have been playing a key role in facilitating software
developers to retrieve existing code fragments from available open-source repositories …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …