6 papers
AgenticSZZ: Temporal Knowledge Graph-Guided Agentic Bug-Inducing Commit Identification
Yu Shi, Hao Li, Bram Adams +1
Identifying Bug-Inducing Commits (BICs) is fundamental for understanding software defects and enabling downstream tasks such as defect prediction and automated program repair. Yet…
HAFix: History-Augmented Large Language Models for Bug Fixing
Yu Shi, Abdul Ali Bangash, Emad Fallahzadeh +2
Recent studies have explored the performance of Large Language Models (LLMs) on various Software Engineering (SE) tasks, such as code generation and bug fixing. However, these appr…
AEGIS: White-Box Attack Path Generation using LLMs and Training Effectiveness Evaluation for Large-Scale Cyber Defence Exercises
Ivan K. Tung, Yu Xiang Shi, Alex Chien +2
Creating attack paths for cyber defence exercises requires substantial expert effort. Existing automation requires vulnerability graphs or exploit sets curated in advance, limiting…
PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice
Yuzhen Shi, Huanghai Liu, Yiran Hu +27
As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential.…
HiCaM: A Hierarchical-Causal Modification Framework for Long-Form Text Modification
Yuntao Shi, Yi Luo, Yeyun Gong +1
Large Language Models (LLMs) have achieved remarkable success in various domains. However, when handling long-form text modification tasks, they still face two major problems: (1)…
Unlocking a New Rust Programming Experience: Fast and Slow Thinking with LLMs to Conquer Undefined Behaviors
Renshuang Jiang, Pan Dong, Zhenling Duan +6
To provide flexibility and low-level interaction capabilities, the unsafe tag in Rust is essential in many projects, but undermines memory safety and introduces Undefined Behaviors…