1 citations · 1 across the 6 of their papers we have counts for
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AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context
Lei Zhang, Yongda Yu, Minghui Yu +11
High-quality evaluation benchmarks are pivotal for deploying Large Language Models (LLMs) in Automated Code Review (ACR). However, existing benchmarks suffer from two critical limi…
Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach
Yongda Yu, Guohao Shi, Xianwei Wu +8
Large Language Models (LLMs) have shown great potential in supporting automated code review due to their impressive capabilities in context understanding and reasoning. However, th…
SynthCoder: Anti-pattern identification and model training for FIM mode code completion
Dongjun Yu, Xiao Yan, Zhenrui Li +6
As a leading application of large language models (LLMs) in software engineering, Fill-in-the-Middle (FIM) mode code completion has drawn wide attention. Training such models requi…
Distilling Desired Comments for Enhanced Code Review with Large Language Models
Yongda Yu, Lei Zhang, Guoping Rong +9
There has been a growing interest in using Large Language Models (LLMs) for code review thanks to their proven proficiency in code comprehension. The primary objective of most revi…
AUCAD: Automated Construction of Alignment Dataset from Log-Related Issues for Enhancing LLM-based Log Generation
Hao Zhang, Dongjun Yu, Lei Zhang +6
Log statements have become an integral part of modern software systems. Prior research efforts have focused on supporting the decisions of placing log statements, such as where/wha…