1 citations · 3 across the 9 of their papers we have counts for
10 papers · 1 filter
MOA: A Profiling-Guided LLM Framework for Memory-Optimization Automation at Codebase Scale
Jiaxi Liang, Yuanxiang Shi, Zezhou Yang +1
Modern large-scale software systems often suffer from pervasive memory inefficiencies (e.g., bloat, churn), leading to excessive resource costs and performance degradation. Existin…
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…
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…
An Empirical Study of Knowledge Distillation for Code Understanding Tasks
Ruiqi Wang, Zezhou Yang, Cuiyun Gao +2
Pre-trained language models (PLMs) have emerged as powerful tools for code understanding. However, deploying these PLMs in large-scale applications faces practical challenges due t…
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…
An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
Zezhou Yang, Sirong Chen, Cuiyun Gao +4
Code generation aims to automatically generate code snippets of specific programming language according to natural language descriptions. The continuous advancements in deep learni…
Bridge and Hint: Extending Pre-trained Language Models for Long-Range Code
Yujia Chen, Cuiyun Gao, Zezhou Yang +2
In the field of code intelligence, effectively modeling long-range code poses a significant challenge. Existing pre-trained language models (PLMs) such as UniXcoder have achieved r…
A Roadmap on Modern Code Review: Challenges and Opportunities
Zezhou Yang, Cuiyun Gao, Zhaoqiang Guo +4
Over the past decade, modern code review (MCR) has been established as a cornerstone of software quality assurance and a vital channel for knowledge transfer within development tea…
Deep Learning Based Code Generation Methods: Literature Review
Zezhou Yang, Sirong Chen, Cuiyun Gao +3
This paper focuses on Code Generation task that aims at generating relevant code fragments according to given natural language descriptions. In the process of software development,…