Ensheng Shi

Orcid: 0000-0002-5543-2025

According to our database1, Ensheng Shi authored at least 16 papers between 2021 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
CoCoAST: Representing Source Code via Hierarchical Splitting and Reconstruction of Abstract Syntax Trees.
Empir. Softw. Eng., November, 2023

SoTaNa: The Open-Source Software Development Assistant.
CoRR, 2023

Towards Efficient Fine-Tuning of Pre-trained Code Models: An Experimental Study and Beyond.
Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2023

You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search.
Proceedings of the IEEE International Conference on Software Maintenance and Evolution, 2023

CoCoSoDa: Effective Contrastive Learning for Code Search.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

2022
A large-scale empirical study of commit message generation: models, datasets and evaluation.
Empir. Softw. Eng., 2022

Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation.
CoRR, 2022

ECMG: Exemplar-based Commit Message Generation.
CoRR, 2022

On the Evaluation of Neural Code Summarization.
Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering, 2022

RACE: Retrieval-augmented Commit Message Generation.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021
Neural Code Summarization: How Far Are We?
CoRR, 2021

Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search.
CoRR, 2021

CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network.
CoRR, 2021

On the Evaluation of Commit Message Generation Models: An Experimental Study.
Proceedings of the IEEE International Conference on Software Maintenance and Evolution, 2021

CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning Approach for Semantic Code Search.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021


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