18 citations · 25 across the 6 of their papers we have counts for
6 papers
CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects
Hanyang Guo, Xunjin Zheng, Zihan Liao +4
Automated code review (CR) is a key application for Large Language Models (LLMs), but progress is hampered by a "reality gap": existing benchmarks evaluate models on isolated sub-t…
Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
Codefuse, Ling Team, : +30
Recent advancements in code large language models (LLMs) have demonstrated remarkable capabilities in code generation and understanding. It is still challenging to build a code LLM…
REPOFUSE: Repository-Level Code Completion with Fused Dual Context
Ming Liang, Xiaoheng Xie, Gehao Zhang +6
The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from…
Principles and Practices of Large-Scale Code Analysis at Ant Group: A Data- and Logic-Oriented Approach
Xiaoheng Xie, Gang Fan, Xiaojun Lin +21
Large-scale software development requires dynamic and multifaceted static code analysis that extends beyond the capabilities of traditional tools. Existing tools like CodeQL lack c…
Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents
Gang Fan, Xiaoheng Xie, Xunjin Zheng +2
The escalating complexity of software systems and accelerating development cycles pose a significant challenge in managing code errors and implementing business logic. Traditional…
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Peng Di, Jianguo Li, Hang Yu +35
Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the…