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20242026
most citedA Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?

3 citations · 3 across the 5 of their papers we have counts for

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cs.SE2026

ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation

QiHong Chen, Aaron Imani, Iftekhar Ahmed

Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conventions. Existing retrieval me…

cs.SE2025

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models

QiHong Chen, Lianghao Jiang, Iftekhar Ahmed

The issue of clone code has persisted in software engineering, primarily because developers often copy and paste code segments. This common practice has elevated the importance of…

cs.SE2025

Enhancing LLM Performance Through Debate: An Empirical Study on Multi-Agent Debate for Coding Tasks

Yong Jin Chun, Qihong Chen, Jiawei Li +1

Large Language Models (LLMs) have advanced autonomous agents' planning and decision-making, yet they struggle with complex tasks requiring diverse expertise and multi-step reasonin…

cs.SE2024★ 3 cited

A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?

QiHong Chen, Jiachen Yu, Jiawei Li +3

Recent advancements in Large Language Models (LLMs) have led to their widespread application in automated code generation. However, these models can still generate defective code t…

cs.SE2024

Does the Order of Fine-tuning Matter and Why?

Qihong Chen, Jiawei Li, Hyunjae Suh +5

To improve the performance on a target task, researchers have fine-tuned language models with an intermediate task before the target task of interest. However, previous works have…