49 citations · 61 across the 14 of their papers we have counts for
15 papers
SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging
Phillip Entin, Wenchao Gu, Alexander Knapp +1
COBOL remains critical across banking, insurance, and government infrastructure. However, maintenance is increasingly challenging due to outdated technologies, sparse documentation…
Evaluating LLMs on Real-World Software Performance Optimization
Ezgi Sarıkayak, Wenchao Gu, Hesham Ghonim +1
Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinement, we still lack benchmarks th…
An Empirical Study of Agent Developer Practices in AI Agent Frameworks
Yanlin Wang, Xinyi Xu, Jiachi Chen +3
The rise of large language models (LLMs) has sparked a surge of interest in agents, leading to the rapid growth of agent frameworks. Agent frameworks are software toolkits and libr…
Weakly Supervised Vulnerability Localization via Multiple Instance Learning
Wenchao Gu, Yupan Chen, Yanlin Wang +3
Software vulnerability detection has emerged as a significant concern in the field of software security recently, capturing the attention of numerous researchers and developers. Mo…
SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Wenchao Gu, Zongyi Lyu, Yanlin Wang +3
Code retrieval aims to provide users with desired code snippets based on users' natural language queries. With the development of deep learning technologies, adopting pre-trained m…
What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
Wenchao Gu, Juntao Chen, Yanlin Wang +6
Repository-level code generation remains challenging due to complex code dependencies and the limitations of large language models (LLMs) in processing long contexts. While retriev…
SECRET: Towards Scalable and Efficient Code Retrieval via Segmented Deep Hashing
Wenchao Gu, Ensheng Shi, Yanlin Wang +5
Code retrieval, which retrieves code snippets based on users' natural language descriptions, is widely used by developers and plays a pivotal role in real-world software developmen…
Search-Based LLMs for Code Optimization
Shuzheng Gao, Cuiyun Gao, Wenchao Gu +1
The code written by developers usually suffers from efficiency problems and contain various performance bugs. These inefficiencies necessitate the research of automated refactoring…
XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection
Yuanhang Yang, Shiyi Qi, Wenchao Gu +3
Sparse models, including sparse Mixture-of-Experts (MoE) models, have emerged as an effective approach for scaling Transformer models. However, they often suffer from computational…
JIANG: Chinese Open Foundation Language Model
Qinhua Duan, Wenchao Gu, Yujia Chen +3
With the advancements in large language model technology, it has showcased capabilities that come close to those of human beings across various tasks. This achievement has garnered…
A Transformer-Based Approach for Improving App Review Response Generation
Weizhe Zhang, Wenchao Gu, Cuiyun Gao +1
Mobile apps are becoming an integral part of people's daily life by providing various functionalities, such as messaging and gaming. App developers try their best to ensure user ex…
CoCoSoDa: Effective Contrastive Learning for Code Search
Ensheng Shi, Yanlin Wang, Wenchao Gu +5
Code search aims to retrieve semantically relevant code snippets for a given natural language query. Recently, many approaches employing contrastive learning have shown promising r…
Accelerating Code Search with Deep Hashing and Code Classification
Wenchao Gu, Yanlin Wang, Lun Du +4
Code search is to search reusable code snippets from source code corpus based on natural languages queries. Deep learning-based methods of code search have shown promising results.…
Source Code Summarization with Structural Relative Position Guided Transformer
Zi Gong, Cuiyun Gao, Yasheng Wang +3
Source code summarization aims at generating concise and clear natural language descriptions for programming languages. Well-written code summaries are beneficial for programmers t…
CRaDLe: Deep Code Retrieval Based on Semantic Dependency Learning
Wenchao Gu, Zongjie Li, Cuiyun Gao +4
Code retrieval is a common practice for programmers to reuse existing code snippets in open-source repositories. Given a user query (i.e., a natural language description), code ret…