Search Results for author: Shuvendu K. Lahiri

Found 9 papers, 4 papers with code

Finding Inductive Loop Invariants using Large Language Models

no code implementations14 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.

Ranking LLM-Generated Loop Invariants for Program Verification

1 code implementation13 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.


Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?

no code implementations3 Oct 2023 Madeline Endres, Sarah Fakhoury, Saikat Chakraborty, Shuvendu K. Lahiri

The emergent abilities of Large Language Models (LLMs) have the potential to facilitate the translation of natural language intent to programmatically checkable assertions.

Fault localization Translation

Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions

no code implementations7 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.

Interactive Code Generation via Test-Driven User-Intent Formalization

no code implementations11 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.

Code Generation

Enabling Open-World Specification Mining via Unsupervised Learning

no code implementations27 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.

Word Embeddings

Formal Specification and Verification of Smart Contracts for Azure Blockchain

1 code implementation20 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

Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces

1 code implementation18 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

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