7 papers
Debug Like a Human: Scaling LLM-based Fault Localization to Processor Design via Block-Level Instruction-Oriented Slicing
Zizhen Liu, Xiaoguang Mao, Deheng Yang +6
Fault localization in modern processor design code is a critical yet time-consuming step during processor verification. While recent advances in LLM-based techniques for module-lev…
Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks
Kang Yang, Xinjun Mao, Shangwen Wang +7
Pre-trained code models rely heavily on high-quality pre-training data, particularly human-written reference comments that bridge code and natural language. However, these comments…
Give LLMs a Security Course: Securing Retrieval-Augmented Code Generation via Knowledge Injection
Bo Lin, Shangwen Wang, Yihao Qin +2
Retrieval-Augmented Code Generation (RACG) leverages external knowledge to enhance Large Language Models (LLMs) in code synthesis, improving the functional correctness of the gener…
Smoke and Mirrors: Jailbreaking LLM-based Code Generation via Implicit Malicious Prompts
Sheng Ouyang, Yihao Qin, Bo Lin +3
The proliferation of Large Language Models (LLMs) has revolutionized natural language processing and significantly impacted code generation tasks, enhancing software development ef…
Large Language Models-Aided Program Debloating
Bo Lin, Shangwen Wang, Yihao Qin +2
As software grows in complexity to accommodate diverse features and platforms, software bloating has emerged as a significant challenge, adversely affecting performance and securit…
Keep It Simple: Towards Accurate Vulnerability Detection for Large Code Graphs
Xin Peng, Shangwen Wang, Yihao Qin +3
Software vulnerability detection is crucial for high-quality software development. Recently, some studies utilizing Graph Neural Networks (GNNs) to learn the graph representation o…