Pre-trained Generative Language models (eg, PLBART, CodeT5, SPT-Code) for source
code yielded strong results on several tasks in the past few years, including code generation …

What aspects of computer programs are represented by the human brain during
comprehension? We leverage brain recordings derived from functional magnetic resonance …

Code naturalness, which captures repetitiveness and predictability in programming
languages, has proven valuable for various code-related tasks in software engineering …

Deep Learning (DL) models to analyze source code have shown immense promise during
the past few years. More recently, self-supervised pre-training has gained traction for …

Taking advantage of the naturalness hypothesis for code, recent development, and research
has focused on applying machine learning (ML) techniques originally developed for natural …

Pre-trained language models are effective in a variety of natural language tasks, but it has
been argued their capabilities fall short of fully learning meaning or understanding …

Programmers often search for usage examples for API methods. A tool that could generate
realistic, idiomatic, and contextual usage examples for one or more APIs would be …

Decompilation, the process of converting machine-level code into readable source code,
plays a critical role in reverse engineering. Given that the main purpose of decompilation is …

A README file plays an essential role as the face of a software project and the initial point of
contact for developers in Open Source Software (OSS) projects. The code snippet ranks …

Compiler fuzzing tools such as Csmith have uncovered many bugs in compilers by randomly
sampling programs from a generative model. The success of these tools is often attributed to …