3 papers
cs.CL2026
CodeScout: Contextual Problem Statement Enhancement for Software Agents
Manan Suri, Xiangci Li, Mehdi Shojaie +5
Current AI-powered code assistance tools often struggle with poorly-defined problem statements that lack sufficient task context and requirements specification. Recent analysis of…
cs.CR2025
From LLMs to Agents: A Comparative Evaluation of LLMs and LLM-based Agents in Security Patch Detection
Junxiao Han, Zheng Yu, Lingfeng Bao +5
The widespread adoption of open-source software (OSS) has accelerated software innovation but also increased security risks due to the rapid propagation of vulnerabilities and sile…
cs.SE2025
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…