The immense amounts of source code provide ample challenges and opportunities during
software development. To handle the size of code bases, developers commonly search for …

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

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

There have been multiple recent proposals on using deep neural networks for code search
using natural language. Common across these proposals is the idea of embedding code …

Learning code representations has found many uses in software engineering, such as code
classification, code search, comment generation, and bug prediction, etc. Although …

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 …

Pre-trained models like BERT have achieved strong improvements on many natural
language processing (NLP) tasks, showing their great generalizability. The success of pre …

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 …

Programmers often write code that has similarity to existing code written somewhere. A tool
that could help programmers to search such similar code would be immensely useful. Such …

Searching and reusing existing code from a large-scale codebase, eg, GitHub, can help
developers complete a programming task efficiently. Recently, Gu et al. proposed a deep …