Search Results for author: Pengyu Nie

Found 8 papers, 4 papers with code

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.

Learning to Generate Code Comments from Class Hierarchies

no code implementations24 Mar 2021 Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Raymond J. Mooney, Junyi Jessy Li, Milos Gligoric

Descriptive code comments are essential for supporting code comprehension and maintenance.

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

2 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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