no code implementations • 14 Nov 2023 • Adharsh Kamath, Aditya Senthilnathan, Saikat Chakraborty, Pantazis Deligiannis, Shuvendu K. Lahiri, Akash Lal, Aseem Rastogi, Subhajit Roy, Rahul Sharma
Finally, we explore the effectiveness of using an efficient combination of a symbolic tool and an LLM on our dataset and compare it against a purely symbolic baseline.
1 code implementation • 13 Oct 2023 • Saikat Chakraborty, Shuvendu K. Lahiri, Sarah Fakhoury, Madanlal Musuvathi, Akash Lal, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, Nikhil Swamy
In this work, we observe that Large Language Models (such as gpt-3. 5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants.
no code implementations • 3 Oct 2023 • Madeline Endres, Sarah Fakhoury, Saikat Chakraborty, Shuvendu K. Lahiri
Informal natural language that describes code functionality, such as code comments or function documentation, may contain substantial information about a programs intent.
1 code implementation • 19 Jun 2023 • Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, Sriram K. Rajamani
We construct a repository-level dataset PragmaticCode for method-completion in Java and evaluate MGD on it.
Ranked #1 on Code Completion on DotPrompts
no code implementations • 7 Apr 2023 • Sarah Fakhoury, Saikat Chakraborty, Madan Musuvathi, Shuvendu K. Lahiri
Several benchmarks have recently emerged to evaluate the ability of LLMs to generate functionally correct code from natural language intent with respect to a set of hidden test cases.
no code implementations • 11 Aug 2022 • Shuvendu K. Lahiri, Sarah Fakhoury, Aaditya Naik, Georgios Sakkas, Saikat Chakraborty, Madanlal Musuvathi, Piali Choudhury, Curtis von Veh, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent.
no code implementations • 27 Apr 2019 • Jordan Henkel, Shuvendu K. Lahiri, Ben Liblit, Thomas Reps
Using this dataset, we show that interesting clusters can be recovered, in a fully automatic way, by leveraging unsupervised learning in the form of word embeddings.
1 code implementation • 20 Dec 2018 • Shuvendu K. Lahiri, Shuo Chen, Yuepeng Wang, Isil Dillig
In this paper, we describe the formal verification of Smart Contracts offered as part of the Azure Blockchain Content and Samples on github.
Programming Languages F.3.1
1 code implementation • 18 Mar 2018 • Jordan Henkel, Shuvendu K. Lahiri, Ben Liblit, Thomas Reps
With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms.
Software Engineering