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 …

Recently, many pre-trained language models for source code have been proposed to model
the context of code and serve as a basis for downstream code intelligence tasks such as …

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

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 …

The problem of fixing errors in programs has attracted substantial interest over the years.
The key challenge for building an effective code fixing tool is to capture a wide range of …

Static analysis tools are widely used for vulnerability detection as they understand programs
with complex behavior and millions of lines of code. Despite their popularity, static analysis …

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 …

We propose Corder, a self-supervised contrastive learning framework for source code
model. Corder is designed to alleviate the need of labeled data for code retrieval and code …

This review article provides an in-depth analysis of the growing field of AI-assisted
programming tasks, specifically focusing on the use of code embeddings and transformers …

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of
code and play a vital role in supporting downstream automated software engineering tasks …