148 citations · 259 across the 43 of their papers we have counts for
28 papers · 1 filter
SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents
Shuzheng Gao, Wenhao Zeng, Zhaojian Yu +5
Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods typically re…
Dependency-Guided Repository-Level C-to-Rust Translation with Reinforcement Alignment
Jia Feng, Wenjie Gan, Cuiyun Gao +5
Automating C-to-Rust migration is critical for improving software security without sacrificing performance. Traditional rule-based methods struggle with diverse C idioms, often pro…
SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades
Man Ho Lam, Chaozheng Wang, Hange Liu +5
Coding agents powered by large language models are increasingly expected to perform realistic software maintenance tasks beyond isolated issue resolution. Existing benchmarks have…
Cascaded Code Editing: Large-Small Model Collaboration for Effective and Efficient Code Editing
Chaozheng Wang, Zezhou Yang, Shuzheng Gao +7
Code editing constitutes a fundamental practice in software development, wherein developers modify existing codebases according to natural language requirements. Accurate code edit…
On the Effectiveness of Context Compression for Repository-Level Tasks: An Empirical Investigation
Jia Feng, Zhanyue Qin, Cuiyun Gao +4
Repository-level code intelligence tasks require large language models (LLMs) to process long, multi-file contexts. Such inputs introduce three challenges: crucial context can be o…
Automated Prompt Generation for Code Intelligence: An Empirical study and Experience in WeChat
Kexing Ji, Shiyun Fu, Cuiyun Gao +4
Large Code Models (LCMs) show potential in code intelligence, but their effectiveness is greatly influenced by prompt quality. Current prompt design is mostly manual, which is time…
TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework
Shuzheng Gao, Eric John Li, Man Ho Lam +5
Large foundation models are fundamentally transforming the software engineering landscape, demonstrating exceptional capabilities across diverse tasks such as code generation, debu…
SEER: Enhancing Chain-of-Thought Code Generation through Self-Exploring Deep Reasoning
Shuzheng Gao, Chaozheng Wang, Cuiyun Gao +1
Code generation, the task of creating executable programs from natural language requirements, has recently seen tremendous advances through Chain-of-Thought (CoT) reasoning, which…
JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
Zhihao Li, Chaozheng Wang, Zongjie Li +12
The WeChat mini-game ecosystem faces rampant intellectual property theft to other platforms via secondary development, yet existing JavaScript obfuscation tools are ill-equipped fo…
A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat
Zezhou Yang, Ting Peng, Cuiyun Gao +3
Code completion, a crucial task in software engineering that enhances developer productivity, has seen substantial improvements with the rapid advancement of large language models…
RAG or Fine-tuning? A Comparative Study on LCMs-based Code Completion in Industry
Chaozheng Wang, Zezhou Yang, Shuzheng Gao +5
Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the e…
The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation
Shuzheng Gao, Chaozheng Wang, Cuiyun Gao +4
Test cases are essential for validating the reliability and quality of software applications. Recent studies have demonstrated the capability of Large Language Models (LLMs) to gen…
Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility
Jialun Cao, Yuk-Kit Chan, Zixuan Ling +12
Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the community interprets model capabilities. In…
A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How
Chaozheng Wang, Shuzheng Gao, Cuiyun Gao +4
API suggestion is a critical task in modern software development, assisting programmers by predicting and recommending third-party APIs based on the current context. Recent advance…
ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code
Jia Feng, Jiachen Liu, Cuiyun Gao +4
In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention. However, existing evaluation benchmarks often focus on limi…
Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach
Yuxuan Wan, Chaozheng Wang, Yi Dong +4
Websites are critical in today's digital world, with over 1.11 billion currently active and approximately 252,000 new sites launched daily. Converting website layout design into fu…
SPVR: syntax-to-prompt vulnerability repair based on large language models
Ruoke Wang, Zongjie Li, Cuiyun Gao +3
Purpose: In the field of vulnerability repair, previous research has leveraged pretrained models and LLM-based prompt engineering, among which LLM-based approaches show better gene…
Exploring Multi-Lingual Bias of Large Code Models in Code Generation
Chaozheng Wang, Zongjie Li, Cuiyun Gao +6
Code generation aims to synthesize code and fulfill functional requirements based on natural language (NL) specifications, which can greatly improve development efficiency. In the…
REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes
Chaozheng Wang, Zongjie Li, Yun Peng +5
Software plays a crucial role in our daily lives, and therefore the quality and security of software systems have become increasingly important. However, vulnerabilities in softwar…
Generative Type Inference for Python
Yun Peng, Chaozheng Wang, Wenxuan Wang +2
Python is a popular dynamic programming language, evidenced by its ranking as the second most commonly used language on GitHub. However, its dynamic type system can lead to potenti…
On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study
Zongjie Li, Chaozheng Wang, Pingchuan Ma +5
Recent advances in large language models (LLMs) significantly boost their usage in software engineering. However, training a well-performing LLM demands a substantial workforce for…
Keeping Pace with Ever-Increasing Data: Towards Continual Learning of Code Intelligence Models
Shuzheng Gao, Hongyu Zhang, Cuiyun Gao +1
Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge…
Practitioners' Expectations on Code Completion
Chaozheng Wang, Junhao Hu, Cuiyun Gao +5
Code completion has become a common practice for programmers during their daily programming activities. It aims at automatically predicting the next tokens or lines that the progra…
CCTEST: Testing and Repairing Code Completion Systems
Zongjie Li, Chaozheng Wang, Zhibo Liu +4
Code completion, a highly valuable topic in the software development domain, has been increasingly promoted for use by recent advances in large language models (LLMs). To date, vis…