Search Results for author: Pengyu Nie

Found 11 papers, 7 papers with code

Multilingual Code Co-Evolution Using Large Language Models

1 code implementation27 Jul 2023 Jiyang Zhang, Pengyu Nie, Junyi Jessy Li, Milos Gligoric

In this paper, we target a novel task: translating code changes from one programming language to another using large language models (LLMs).

Learning Deep Semantics for Test Completion

1 code implementation20 Feb 2023 Pengyu Nie, Rahul Banerjee, Junyi Jessy Li, Raymond J. Mooney, Milos Gligoric

We formalize the novel task of test completion to automatically complete the next statement in a test method based on the context of prior statements and the code under test.

Code Completion Code Generation

CoditT5: Pretraining for Source Code and Natural Language Editing

1 code implementation10 Aug 2022 Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Jessy Li, Milos Gligoric

Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits.

Bug fixing Language Modelling +1

A Framework for Multi-stage Bonus Allocation in meal delivery Platform

no code implementations22 Feb 2022 Zhuolin Wu, Li Wang, Fangsheng Huang, Linjun Zhou, Yu Song, Chengpeng Ye, Pengyu Nie, Hao Ren, Jinghua Hao, Renqing He, Zhizhao Sun

The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to order and its acceptance probability, the Lagrangian dual-based dynamic programming algorithm aims to calculate the empirical Lagrangian multiplier for each allocation stage offline based on the historical data set, and the online allocation algorithm uses the results attained in the offline part to calculate a proper delivery bonus for each order.

Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning

1 code implementation1 Mar 2021 Pengyu Nie, Karl Palmskog, Junyi Jessy Li, Milos Gligoric

Naming conventions are an important concern in large verification projects using proof assistants, such as Coq.


Leveraging Class Hierarchy for Code Comprehension

no code implementations NeurIPS Workshop CAP 2020 Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Li, Ray Mooney, Milos Gligoric

Object-oriented programming languages enable a hierarchical class structure, which provides rich contextual information to guide code comprehension and synthesis.

Learning to Format Coq Code Using Language Models

no code implementations18 Jun 2020 Pengyu Nie, Karl Palmskog, Junyi Jessy Li, Milos Gligoric

Should arguments to the rewrite tactic be separated by a single space?

Learning to Update Natural Language Comments Based on Code Changes

1 code implementation ACL 2020 Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney

We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies.

Deep Generation of Coq Lemma Names Using Elaborated Terms

3 code implementations16 Apr 2020 Pengyu Nie, Karl Palmskog, Junyi Jessy Li, Milos Gligoric

Our results show that Roosterize substantially outperforms baselines for suggesting lemma names, highlighting the importance of using multi-input models and elaborated terms.


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