15 citations · 41 across the 6 of their papers we have counts for
5 papers
Finding Inductive Loop Invariants using Large Language Models
Adharsh Kamath, Aditya Senthilnathan, Saikat Chakraborty +6
Loop invariants are fundamental to reasoning about programs with loops. They establish properties about a given loop's behavior. When they additionally are inductive, they become u…
CONCORD: Clone-aware Contrastive Learning for Source Code
Yangruibo Ding, Saikat Chakraborty, Luca Buratti +4
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 learning…
Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions
Sarah Fakhoury, Saikat Chakraborty, Madan Musuvathi +1
Large language models (LLMs), such as OpenAI's Codex, have demonstrated their potential to generate code from natural language descriptions across a wide range of programming tasks…
On ML-Based Program Translation: Perils and Promises
Aniketh Malyala, Katelyn Zhou, Baishakhi Ray +1
With the advent of new and advanced programming languages, it becomes imperative to migrate legacy software to new programming languages. Unsupervised Machine Learning-based Progra…
NatGen: Generative pre-training by "Naturalizing" source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding +2
Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and…