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P2 PF2UML:A Tool for Problem-Oriented Requirements Modeling and Transformation

PF2UML:一个面向问题需求建模与转换的工具
FSE Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering2025匹配度 1/10 · 该论文关注基于问题框架的需求建模与自动转换为UML,属于需求工程和建模工具,与用户核心方向(静态分析、Agentic程序、浮点程序分析、贝叶斯程序分析及其与LLM交叉)没有直接关联。引用 1 inbox
Hongbin Xiao, Zhi Li, Yilong Yang, Fei Tang, Dongming Jin
crossref: 2026-09-12

AI 速览

一句话:PF2UML 支持基于问题框架的需求建模,并自动转换为 UML 图,提升效率与一致性。

需求工程中,从需求分析过渡到系统设计常面临需求表达歧义、理解偏差和手工操作低效等问题。本文提出 PF2UML 工具,旨在衔接需求分析与系统设计。其核心功能包括:基于问题框架(Problem Frames)理论支持面向问题的需求建模,并自动将需求模型转换为 UML 图。评估表明,PF2UML 在需求建模时间效率上比传统手工方法提升 34%,显著提高模型转换效率,并有效保证从需求建模到系统设计的一致性;六个案例研究显示,在复杂场景下其效率达到手工流程的 10 倍以上。

问题
需求分析到系统设计过渡中存在需求表达歧义、理解偏差与手工操作低效等问题。
方法
实现基于 Problem Frames 理论的需求建模工具,并自动化地将需求模型转换为 UML 图。
评估
通过实验评估与六个案例研究,比较时间效率、模型转换效率及需求到设计的一致性。
原文摘要(Abstract)

Requirements engineering is a critical phase in ensuring the success of software development projects. However, transitioning from requirement analysis to system design often encounters challenges, including ambiguity in requirement expression, understanding deviations, and inefficiencies caused by manual operations. To address these issues, we propose PF2UML, a tool designed to bridge the gap between requirements analysis and system design. PF2UML offers three core features: (1) support for problem-oriented requirements modeling based on Problem Frames theory, (2) automated transformation of requirement models into UML diagrams. Experimental evaluations demonstrate that PF2UML significantly outperforms traditional manual methods, achieving improvements of 34% in time efficiency for requirements modeling, significantly improving the efficiency of model transformation, and effectively ensuring the consistency from requirements modeling to system design. Moreover, six case studies reveal that PF2UML achieves more than 10 times the efficiency of manual processes in complex scenarios.

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BibTeX
@inproceedings{paperbot9092,
  title = {PF2UML:A Tool for Problem-Oriented Requirements Modeling and Transformation},
  author = {Hongbin Xiao and Zhi Li and Yilong Yang and Fei Tang and Dongming Jin},
  booktitle = {FSE},
  year = {2025},
  doi = {10.1145/3696630.3728596}
}