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20212026
most citedLibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations

1 citations · 2 across the 7 of their papers we have counts for

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cs.SE2026

EpiDroid: Dependency-Guided Recomposition for Deep State Discovery in Mobile GUI Testing

Jiahui Song, Jiaxin Zhi, Kangjia Zhao +6

The increasing scale and complexity of mobile applications make automated GUI exploration essential for software quality assurance. However, existing methods often neglect state de…

cs.SE2026

CodeGlance: Understanding Code Reasoning Challenges in LLMs through Multi-Dimensional Feature Analysis

Yunkun Wang, Xuanhe Zhang, Junxiao Han +2

In modern software development, developers frequently need to understand code behavior at a glance -- whether reviewing pull requests, debugging issues, or navigating unfamiliar co…

cs.SE2026

Can Vision-Language Models Handle Long-Context Code? An Empirical Study on Visual Compression

Jianping Zhong, Guochang Li, Chen Zhi +6

Large Language Models (LLMs) struggle with long-context code due to window limitations. Existing textual code compression methods mitigate this via selective filtering but often di…

cs.SE2025

Revisiting Vulnerability Patch Localization: An Empirical Study and LLM-Based Solution

Haoran Xu, Chen Zhi, Junxiao Han +3

Open-source software vulnerability patch detection is a critical component for maintaining software security and ensuring software supply chain integrity. Traditional manual detect…

cs.SE2025

GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion

Xingliang Wang, Baoyi Wang, Chen Zhi +4

LLMs excel in localized code completion but struggle with repository-level tasks due to limited context windows and complex semantic and structural dependencies across codebases. W…

cs.SE20251 cited

LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations

Junxiao Han, Yarong Wang, Xiaodong Gu +5

In this paper, we propose LibRec, a novel framework that integrates the capabilities of LLMs with retrieval-augmented generation(RAG) techniques to automate the recommendation of a…