5 citations · 5 across the 1 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…
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…