Researchers have recently achieved significant advances in deep learning techniques,
which in turn has substantially advanced other research disciplines, such as natural …

Recent years have seen the successful application of large pre-trained models to code
representation learning, resulting in substantial improvements on many code-related …

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

Predicting function names in stripped binaries is an extremely useful but challenging task, as
it requires summarizing the execution behavior and semantics of the function in human …

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 …

Source code summaries are important for program comprehension and maintenance.
However, there are plenty of programs with missing, outdated, or mismatched summaries …

To support software developers in understanding and maintaining programs, various
automatic code summarization techniques have been proposed to generate a concise …

While commonsense knowledge acquisition and reasoning has traditionally been a core
research topic in the knowledge representation and reasoning community, recent years …

Code comment generation aims at generating natural language descriptions for a code
snippet to facilitate developers' program comprehension activities. Despite being studied for …

Context: Most defect prediction methods consider a series of traditional manually designed
static code metrics. However, only using these hand-crafted features is impractical. Some …