1 citations · 2 across the 4 of their papers we have counts for
6 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
CRJudgeBench: Can AI Detect Plausible but Invalid Code Reviews?
Yue Pan, Jiawei Li, Ziyuan Zhang +2
Large language models can generate plausible code-review comments, but such comments may contain technically incorrect claims that mislead developers. We study technical trustworth…
Echo: Graph-Enhanced Retrieval and Execution Feedback for Issue Reproduction Test Generation
Zhiwei Fei, Yue Pan, Federica Sarro +4
Identifying the root cause of a bug remains difficult for many developers because bug reports often lack a bug reproducing test case that reliably triggers the failure. Manually wr…
Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories
Oorja Majgaonkar, Zhiwei Fei, Xiang Li +2
The increasing deployment of Large Language Model (LLM) agents for complex software engineering tasks has created a need to understand their problem-solving behaviours beyond simpl…
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…
Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
Haoxiang Jia, Robbie Morris, He Ye +2
The growing use of large language models (LLMs) has increased the importance of natural language (NL) in software engineering. However, ambiguity of NL can harm software quality, a…