9 citations · 28 across the 10 of their papers we have counts for
10 papers
Learning from the Test: Self-Referential Differential Testing for Deep RL Agents
Junda He, Jieke Shi, Zhou Yang +2
Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in real-world applications, ensurin…
Backdoors in Code Summarizers: How Bad Is It?
Chenyu Wang, Zhou Yang, Yaniv Harel +1
Code LLMs are increasingly employed in software development. However, studies have shown that they are vulnerable to backdoor attacks: when a trigger (a specific input pattern) app…
Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies
Jieke Shi, Junda He, Zhou Yang +2
With the increasing use of neural policies in control systems, ensuring their safety and reliability has become a critical software engineering task. One prevalent approach to ensu…
Hotfixing Large Language Models for Code
Zhou Yang, David Lo
Large Language Models for Code (LLM4Code) have become an integral part of developers' workflows, assisting with tasks such as code completion and generation. However, these models…
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation
Zhensu Sun, Xiaoning Du, Zhou Yang +2
Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models…
Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead
Jieke Shi, Zhou Yang, David Lo
Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software…
Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code
Zhou Yang, Zhensu Sun, Terry Zhuo Yue +2
Large language models for code (LLM4Code), which demonstrate strong performance (e.g., high accuracy) in processing source code, have significantly transformed software engineering…
Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions
Chenyu Wang, Zhou Yang, Yunbo Lyu +3
Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness ha…
Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong +6
Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of…
Gotcha! This Model Uses My Code! Evaluating Membership Leakage Risks in Code Models
Zhou Yang, Zhipeng Zhao, Chenyu Wang +4
Given large-scale source code datasets available in open-source projects and advanced large language models, recent code models have been proposed to address a series of critical s…