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 …

Code completion is an essential feature of IDEs, yet current auto-completers are restricted to
either grammar-based or NLP-based single token completions. Both approaches have …

Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research focused on …

Code review is an essential activity for ensuring the quality and maintainability of software
projects. However, it is a time-consuming and often error-prone task that can significantly …

Large language models of code (Code-LLMs) have recently brought tremendous advances
to code completion, a fundamental feature of programming assistance and code …

Computer programming has emerged in research, industry, and everyday life as a general-
purpose problem-solving tool. From this expansion, there has been a steady rise in demand …

Transformer-based language models for automatic code completion have shown great
promise so far, yet the evaluation of these models rarely uses real data. This study provides …

Pre-trained language models (PLMs) have become a prevalent technique in deep learning
for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general …

In this work, we propose an approach for collecting completion usage logs from the users in
an IDE and using them to train a machine learning based model for ranking completion …