5 citations · 9 across the 11 of their papers we have counts for
17 papers
Compartment-driven Gaussian processes for uncertainty quantification of pharmacokinetic exposures
Sanjay Palta-Hill, Barbara Engelhardt, Edric Tam
Pharmacokinetic modeling is a crucial part of drug development. An important goal is to estimate patients' total and peak exposure to a drug over time, quantified by the area under…
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications?
Zhiqing Zhong, Mingyu Chen, Aoyang Fang +5
As coding agents become more capable, tests remain the primary evidence for judging whether their code is correct. Yet passing available tests does not establish that an implementa…
Do Tool Calls Execute as Intended? Measuring and Repairing Intent-Execution Correspondence in LLM Agents
Boyang Yang, Zhenhao Li, Ziyao Yang +4
Agents built on large language models (LLMs) build and run software through tool calls. A call reaches its program through several hops, and any hop can change the call without not…
Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair
Wenqiang Luo, Jacky Keung, Xiaoyu Shi +4
Large language models (LLMs) have advanced automatic program repair (APR) to the point where agentic systems routinely resolve real-world, repository-level issues. Yet the generate…
A Single Patch Is Not Enough: Deterministic Fusion of Repair Candidates
Boyang Yang, Xiangliang Hu, Luyao Ren +4
Modern LLM coding agents are commonly evaluated using pass@k, but developers typically apply a single final patch in real-world settings. This pass@k-to-pass@1 gap is a post-genera…
PAFT: Preservation Aware Fine-Tuning for Minimal-Edit Program Repair
Boyang Yang, Zijian Cai, Shunfu Jin +1
Large language models (LLMs) are effective for automated program repair, but plausible patches that pass the full test suite often rewrite more code than necessary, increasing revi…
Beyond Localization: Recoverable Headroom and Residual Frontier in Repository-Level RAG-APR
Pengtao Zhao, Boyang Yang, Bach Le +2
Repository-level automated program repair (APR) increasingly treats stronger localization as the main path to better repair. We ask a more targeted question: once localization is s…
A Taxonomy of Prompt Defects in LLM Systems
Haoye Tian, Chong Wang, BoYang Yang +2
Large Language Models (LLMs) have become key components of modern software, with prompts acting as their de-facto programming interface. However, prompt design remains largely empi…
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios
Junkai Chen, Huihui Huang, Yunbo Lyu +10
Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benc…
Input Reduction Enhanced LLM-based Program Repair
Boyang Yang, Luyao Ren, Xin Yin +3
Large Language Models (LLMs) have shown great potential in Automated Program Repair (APR). Test inputs, being crucial for reasoning the root cause of failures, are always included…
A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications
Boyang Yang, Zijian Cai, Fengling Liu +5
Large language models (LLMs) are reshaping automated program repair. We present a reproducible hierarchical taxonomy that organizes 66 LLM-based repair systems according to where r…
A Systematic Survey on Debugging Techniques for Machine Learning Systems
Thanh-Dat Nguyen, Haoye Tian, Bach Le +2
Debugging ML software (i.e., the detection, localization and fixing of faults) poses unique challenges compared to traditional software largely due to the probabilistic nature and…
Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement
Wenqiang Luo, Jacky Wai Keung, Boyang Yang +4
Recent advances in leveraging LLMs for APR have demonstrated impressive capabilities in fixing software defects. However, current LLM-based approaches predominantly focus on mainst…
Enhancing repository-level software repair via repository-aware knowledge graphs
Boyang Yang, Jiadong Ren, Shunfu Jin +4
Repository-level software repair faces challenges in bridging semantic gaps between issue descriptions and code patches. Existing approaches, which primarily rely on large language…
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
Wenqiang Luo, Jacky Wai Keung, Boyang Yang +5
Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant losses in resources consumed by software maintenance. Rece…
Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
Aidan Z. H. Yang, Haoye Tian, He Ye +2
Software security vulnerabilities allow attackers to perform malicious activities to disrupt software operations. Recent Transformer-based language models have significantly advanc…
ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts
Lyuye Zhang, Kaixuan Li, Kairan Sun +4
Smart contracts are susceptible to various security issues, among which access control (AC) vulnerabilities are particularly critical. While existing research has proposed multiple…