most citedA Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat

1 citations · 1 across the 3 of their papers we have counts for

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5 papers

cs.SE2025

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…

cs.SE2025

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…

cs.CR2025

JSidentify-V2: Leveraging Dynamic Memory Fingerprinting for Mini-Game Plagiarism Detection

Zhihao Li, Chaozheng Wang, Zongjie Li +9

The explosive growth of mini-game platforms has led to widespread code plagiarism, where malicious users access popular games' source code and republish them with modifications. Wh…

cs.SE20251 cited

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…

cs.SE2025

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…