55 citations · 96 across the 28 of their papers we have counts for
11 papers · 1 filter
LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer
Kunpeng Zhang, Dongwei Xiao, Daoyuan Wu +5
Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing DL fuzzing techniques have mad…
Understanding and Bridging the Planner-Coder Gap: A Systematic Study on the Robustness of Multi-Agent Systems for Code Generation
Zongyi Lyu, Songqiang Chen, Zhenlan Ji +5
Multi-agent systems (MASs) have emerged as a promising paradigm for automated code generation, demonstrating impressive performance on established benchmarks. Despite their prosper…
Digging Into the Internal: Causality-Based Analysis of LLM Function Calling
Zhenlan Ji, Daoyuan Wu, Wenxuan Wang +3
Function calling (FC) has emerged as a powerful technique for facilitating large language models (LLMs) to interact with external systems and perform structured tasks. However, the…
Low-Cost and Comprehensive Non-textual Input Fuzzing with LLM-Synthesized Input Generators
Kunpeng Zhang, Zongjie Li, Daoyuan Wu +2
Modern software often accepts inputs with highly complex grammars. Recent advances in large language models (LLMs) have shown that they can be used to synthesize high-quality natur…
API-guided Dataset Synthesis to Finetune Large Code Models
Zongjie Li, Daoyuan Wu, Shuai Wang +1
Large code models (LCMs), pre-trained on vast code corpora, have demonstrated remarkable performance across a wide array of code-related tasks. Supervised fine-tuning (SFT) plays a…
PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation
Ye Liu, Yue Xue, Daoyuan Wu +4
With recent advances in large language models (LLMs), this paper explores the potential of leveraging state-of-the-art LLMs,such as GPT-4, to transfer existing human-written proper…