paper-with-me

홈 › Papers

OSS-UAgent: An Agent-based Usability Evaluation Framework for Open Source Software

2025-05-29 · Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai, Peng Cheng, Wenyuan Yu, Wenjie Zhang, Xuemin Lin, Jingren Zhou

Usability evaluation is critical to the impact and adoption of open source software (OSS), yet traditional methods relying on human evaluators suffer from high costs and limited scalability. To address these limitations, we introduce OSS-UAgent, an automated, configurable, and interactive agent-based usability evaluation framework specifically designed for open source software. Our framework employs intelligent agents powered by large language models (LLMs) to simulate developers performing programming tasks across various experience levels (from Junior to Expert). By dynamically constructing platform-specific knowledge bases, OSS-UAgent ensures accurate and context-aware code generation. The generated code is automatically evaluated across multiple dimensions, including compliance, correctness, and readability, providing a comprehensive measure of the software's usability. Additionally, our demonstration showcases OSS-UAgent's practical application in evaluating graph analytics platforms, highlighting its effectiveness in automating usability evaluation.

📄 PDF Abstract BibTeX arXiv:2505.23239

Code (1)

lingkai981/oss-uagent 공식 구현

Tasks

Code Generation

Similar Papers 제목 키워드 기반

Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector

2024-06-17 · Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Hongzhi Zhang 외

Hallucination detection is a challenging task for large language models (LLMs), and existing studies heavily rely on powerful closed-source LLMs such as GPT-4. In this paper, we propose an autonomous LLM-based agent fram…

2kHallucination

EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation

2025-11-08 · Rui Jia, Min Zhang, Fengrui Liu, Bo Jiang 외 arxiv

In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirements while supporting adaptive assessment and learning. Existing LLM-based sin…

Question Generation

GuruAgents: Emulating Wise Investors with Prompt-Guided LLM Agents

2025-10-02 · Yejin Kim, Youngbin Lee, Juhyeong Kim, Yongjae Lee arxiv

This study demonstrates that GuruAgents, prompt-guided AI agents, can systematically operationalize the strategies of legendary investment gurus. We develop five distinct GuruAgents, each designed to emulate an iconic in…

Prompt Engineering

InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation

2025-08-05 · Wen-Xi Yang, Tian-Fang Zhao, Guan Liu, Liang Yang 외 arxiv

Collaborative partnership matters in inquiry-oriented education. However, most study partners are selected either rely on experience-based assignments with little scientific planning or build on rule-based machine assist…

SimuAgent: An LLM-Based Simulink Modeling Assistant Enhanced with Reinforcement Learning

2026-01-08 · Yanchang Liang, Xiaowei Zhao arxiv

Large language models (LLMs) have revolutionized text-based code automation, but their potential in graph-oriented engineering workflows remains under-explored. We introduce SimuAgent, an LLM-powered modeling and simulat…

Reinforcement LearningData Augmentation