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P2 Towards Generating Labeled Property Graphs for Comprehending C#-based Software Projects

面向理解基于 C# 的软件项目而生成带标签属性图
ASE Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering2022匹配度 2/10 · 该论文面向C#项目的软件理解,生成标签属性图以辅助XR开发者,属于程序分析与开发者工具,但未涉及LLM/Agent、机器学习系统静态分析、浮点程序分析或贝叶斯程序分析等用户核心方向。引用 2 inbox
David Heidrich, Andreas Schreiber, Sebastian Oberdörfer
crossref: 2026-09-12

AI 速览

一句话:提出 Src2Neo,将 C# 项目生成每个软件实体对应单一节点的带标签属性图,辅助 XR 开发者理解组件关系。

C# 是 XR 开发者最广泛使用的编程语言,但面向 C# 软件的图数据获取工具仍然有限。XR 开发常依赖复用现有组件,因此需要通过图可视化来理解组件间关系。本文提出 Src2Neo,一个为 C# 软件项目生成带标签属性图(labeled property graph)的工具。与既有方案不同,它将每个软件实体映射为恰好一个节点,并采用简单的 C# 命名方案,使生成图更易读。该图既能帮助开发者理解软件组件及其关系,也可作为更高级软件可视化的数据基础,从而避免复杂的数据请求。

问题
C# 在 XR 开发者中广泛使用,但面向 C# 软件的图数据获取工具有限;XR 开发依赖复用现有组件,需要理解组件间关系。
方法
提出 Src2Neo,为 C# 软件项目生成带标签属性图,采用简单 C# 命名方案,并将每个软件实体映射到恰好一个节点。
评估
摘要未报告量化实验或用户研究;通过提供易读的软件组件图表示,并作为高级软件可视化的数据基础来体现工具用途。
原文摘要(Abstract)

C# is the most widely used programming language among XR developers. However, only a limited number of graph-based data acquisition tools exist for C# software. XR development commonly relies on reusing existing software components to accelerate development. Graph-based visualization tools can facilitate this comprehension process, e.g., by providing an overview of relationships between components. This work describes a new tool called Src2Neo that generates labeled property graphs of C#-based software projects. The stored graph follows a simple C# naming scheme and — contrary to other solutions — maps each software entity to exactly one node. The resulting graph facilitates the comprehension process by providing an easy to read representation of software components. Additionally, the generated graphs can act as a data basis for more advanced software visualizations without the need for complex data requests.

链接与引用

DOI 原文 · PDF(开放获取) ·

BibTeX
@inproceedings{paperbot7273,
  title = {Towards Generating Labeled Property Graphs for Comprehending C#-based Software Projects},
  author = {David Heidrich and Andreas Schreiber and Sebastian Oberdörfer},
  booktitle = {ASE},
  year = {2022},
  doi = {10.1145/3551349.3560513}
}