3 citations · 10 across the 12 of their papers we have counts for
13 papers
OpenRUA: Robot-Use Agents Are Zero-Shot Visuomotor Policies
Zhaoyang Chu, Earl T. Barr, Claire Le Goues +4
Coding agents are extending their reach into the physical world by writing and executing robot control programs. One might expect the agents to use the existing mature software sta…
Disorder-induced conducting edges on Kagomé lattice
A. Chmeruk, D. Jones, L. Chioncel
Within a cluster extension of the coherent potential approximation, disorder averaging generates a non-local self-energy that renormalizes both diagonal and off-diagonal hopping te…
COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation
Jingzhi Gong, Jie M. Zhang, Gunel Jahangirova +3
Code generation systems make each LLM call with a model, a prompt, and decoding settings. However, existing optimization methods usually tune only part of these choices or use one…
TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks
Zhaoyang Chu, Jiarui Hu, Xingyu Jiang +8
We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 ter…
EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents
Yaoqi Guo, Ying Xiao, Jie M. Zhang +4
Software engineering (SE) agents powered by large language models are increasingly adopted in practice, yet they often incur substantial monetary cost. We introduce EET, an experie…
HotBugs.jar: A Benchmark of Hot Fixes for Time-Critical Bugs
Carol Hanna, Federica Sarro, Mark Harman +1
Hot fixes are urgent, unplanned changes deployed to production systems to address time-critical issues. Despite their importance, no existing evaluation benchmark focuses specifica…
Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries
Lukas Twist, Mark Harman, Helen Yannakoudakis +1
Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent libraries. Such library hallucinations…
Generative AI for Testing of Autonomous Driving Systems: A Survey
Qunying Song, He Ye, Mark Harman +1
Autonomous driving systems (ADS) have been an active area of research, with the potential to deliver significant benefits to society. However, before large-scale deployment on publ…
Benchmarking LLMs for Unit Test Generation from Real-World Functions
Dong Huang, Jie M. Zhang, Mark Harman +3
Recently, large language models (LLMs) have shown great promise in automating unit test generation, significantly reducing the manual effort required by developers. To effectively…
A Study of LLMs' Preferences for Libraries and Programming Languages
Lukas Twist, Mark Harman, Don Syme +4
Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make…
Measuring the Influence of Incorrect Code on Test Generation
Dong Huang, Jie M. Zhang, Mark Harman +2
It is natural to suppose that a Large Language Model is more likely to generate correct test cases when prompted with correct code under test, compared to incorrect code under test…
A Comprehensive Study on Large Language Models for Mutation Testing
Bo Wang, Mingda Chen, Ming Deng +4
Large Language Models (LLMs) have recently been used to generate mutants in both research work and in industrial practice. However, there has been no comprehensive empirical study…
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
Kaibo Liu, Zhenpeng Chen, Yiyang Liu +7
Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this probl…