Recently, machine learning (ML) methods have been used to create powerful language
models for a broad range of natural language processing tasks. An important subset of this …

Program synthesis or code generation aims to generate a program that satisfies a problem
specification. Recent approaches using large-scale pretrained language models (LMs) have …

Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist
programmers or even generate programs themselves could make programming more …

Large pre-trained code generation models, such as OpenAI Codex, can generate syntax-
and function-correct code, making the coding of programmers more productive and our …

Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently
shown to transfer well to Programming Languages (PL) and largely benefit a broad set of …

Automatic program synthesis is a long-lasting dream in software engineering. Recently, a
promising Deep Learning (DL) based solution, called Copilot, has been proposed by …

Code summarization and generation empower conversion between programming language
(PL) and natural language (NL), while code translation avails the migration of legacy code …

Benchmark datasets have a significant impact on accelerating research in programming
language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster …

While programming is one of the most broadly applicable skills in modern society, modern
machine learning models still cannot code solutions to basic problems. Despite its …

Source code summarization refers to the natural language description of the source code's
function. It can help developers easily understand the semantics of the source code. We can …