1 citations · 1 across the 11 of their papers we have counts for
11 papers
Breaking Customized LLMs for Coding: Automated Red Teaming for Instruction Backdoor Attacks
Yuchen Chen, Wei Cheng, Yuan Xiao +5
LLM customization platforms allow users to build task-specific models for code intelligence tasks by embedding instructions into system prompts, without modifying the underlying mo…
Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation
Yuchen Chen, Wei Cheng, Yuan Xiao +7
LLM-based systems increasingly incorporate long-term memory to improve cross-session continuity. However, once insecure coding preferences are stored, they may silently influence s…
Rise From The Ashes: LLM-based Static Analysis for Deep Learning Framework Bugs
Shaoyu Yang, Haifeng Lin, Chunrong Fang +6
Deep learning (DL) frameworks are critical AI infrastructures that often hide bugs with serious security implications. While dynamic approaches such as fuzzing are effective in unc…
Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization
Yuhan Wu, Huan Zhang, Wei Cheng +3
LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offer…
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing
Wei Cheng, Yongchang Cao, Chen Shen +4
Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bottlenecks, posing challenges f…
Self-Improving Code Generation via Semantic Entropy and Behavioral Consensus
Huan Zhang, Wei Cheng, Wei Hu
Improving the code generation capabilities of large language models (LLMs) typically relies on supervised fine-tuning or preference optimization, both of which require costly exter…
Bridging the Knowledge Void: Inference-time Acquisition of Unfamiliar Programming Languages for Coding Tasks
Chen Shen, Wei Cheng, Jingyue Yang +3
The proficiency of Large Language Models (LLMs) in coding tasks is often a reflection of their extensive pre-training corpora, which typically collapses when confronted with previo…
Bootstrapping Code Translation with Weighted Multilanguage Exploration
Yuhan Wu, Huan Zhang, Wei Cheng +3
Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data paired with executable test oracles, and o…
A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement
Huan Zhang, Wei Cheng, Yuhan Wu +1
Large language models (LLMs) have achieved impressive performance on code generation. Although prior studies enhanced LLMs with prompting techniques and code refinement, they still…
Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion
Wei Cheng, Yuhan Wu, Wei Hu
Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion. Yet, it is challenging for pre-trained LMs to g…
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph
Wei Cheng, Xiangrong Zhu, Wei Hu
Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers o…