Chong Wang
Fudan University
9 Papers
Chong Wang is an academic researcher from Fudan University. The author has contributed to research in topics: Computer science & Code (cryptography). The author has an hindex of 1, co-authored 2 publications.
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Papers
A learning-based approach for automatic construction of domain glossary from source code and documentation
Chong Wang,Xin Peng,Mingwei Liu,Zhenchang Xing,Xuefang Bai,Bing Xie,Tuo Wang +6 more
- 12 Aug 2019
TL;DR: This paper proposes a learning-based approach for automatic construction of domain glossary from source code and software documentation that uses a set of high-quality seed terms identified from code identifiers and natural language concept definitions to train a domain-specific prediction model to recognize glossary terms based on the lexical and semantic context of the sentences mentioning domain- specific concepts.
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Boosting Static Resource Leak Detection via LLM-based Resource-Oriented Intention Inference
Chong Wang,Jianan Liu,Xin Peng,Yang Liu,Yiling Lou +4 more
TL;DR: This work proposes InferROI, a novel approach that leverages large language models (LLMs) to directly infer resource-oriented intentions (acquisition, release, and reachability validation) in code, based on resource management knowledge and code context understanding, rather than mechanical API matching.
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DeepAnna: Deep Learning based Java Annotation Recommendation and Misuse Detection
Yi Liu,Yadong Yan,Chaofeng Sha,Xin Peng,Bihuan Chen,Chong Wang +5 more
- 01 Mar 2022
TL;DR: An empirical study on Stack Overflow questions is conducted to investigate the major development frameworks that are involved in questions about Java annotations and the main problems encountered by developers in the use ofjava annotations and proposes DeepAnna, a deep learning based Java annotation recommendation and misuse detection approach.
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Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
Junan Zhang,Kaifeng Huang,Bihuan Chen,Chong Wang,Zhenhao Tian,Xin Peng +5 more
TL;DR: Cerebro is proposed and implemented to detect malicious packages in NPM and PyPI and curate a feature set based on a high-level abstraction of malicious behavior to enable multi-lingual knowledge fusing and fine-tune the BERT model to understand the semantics of maliciousbehavior.
Learning based and Context Aware Non-Informative Comment Detection
Mingwei Liu,Yanjun Yang,Xin Peng,Chong Wang,Chengyuan Zhao,Xin Wang,Shuangshuang Xing +6 more
- 01 Sep 2020
TL;DR: The approach that is introduced is designed and implemented for the DeClutter challenge of Doc-Gen2, which detects non-informative code comments, and combines both comment based text classification and code context based prediction.
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