activity
20242026
collaborators

22 papers

cs.SE2026

MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems

Aaron Guoxiang Guo, Aldeida Aleti, Neelofar Neelofar +3

With the widespread application of LLM-based dialogue systems in daily life, quality assurance has become more important than ever. Recent research has successfully introduced meth…

cs.SE2026

Evaluating Large Language Models for Multilingual Vulnerability Detection at Dual Granularities

Honglin Shu, Michael Fu, Junji Yu +4

Various deep learning-based approaches utilizing pre-trained language models (PLMs) have been proposed for automated vulnerability detection. With recent advancements in large lang…

cs.CR2026

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam +3

Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, de…

cs.SE2025

When AI Takes the Wheel: Security Analysis of Framework-Constrained Program Generation

Yue Liu, Zhenchang Xing, Shidong Pan +1

In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex framework-constrained software systems based on t…

cs.SE2025

What Types of Code Review Comments Do Developers Most Frequently Resolve?

Saul Goldman, Hong Yi Lin, Jirat Pasuksmit +11

Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes.…

cs.SE2025

DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3

Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an i…