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20232026
most citedAI-powered Code Review with LLMs: Early Results

6 citations · 14 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.SE2025

PatchSeeker: Mapping NVD Records to their Vulnerability-fixing Commits with LLM Generated Commits and Embeddings

Huu Hung Nguyen, Anh Tuan Nguyen, Thanh Le-Cong +8

Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database (NVD) is the authoritative source for cataloging these vulnerab…

cs.SE2025★ 1 cited

Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions

Amirali Sajadi, Binh Le, Anh Nguyen +2

The widespread adoption of conversational LLMs for software development has raised new security concerns regarding the safety of LLM-generated content. Our motivational study outli…

cs.SE2024★ 5 cited

Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++

Anh The Nguyen, Triet Huynh Minh Le, M. Ali Babar

Background: The C and C++ languages hold significant importance in Software Engineering research because of their widespread use in practice. Numerous studies have utilized Machine…

cs.SE2024★ 6 cited

AI-powered Code Review with LLMs: Early Results

Zeeshan Rasheed, Malik Abdul Sami, Muhammad Waseem +5

In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potent…

cs.SE2024★ 1 cited

Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals

Khanh Nghiem, Anh Minh Nguyen, Nghi D. Q. Bui

As a research-product hybrid group in AI for Software Engineering (AI4SE), we present four key takeaways from our experience developing in-IDE AI coding assistants. AI coding assis…