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P2 PolyFax: a toolkit for characterizing multi-language software

PolyFax:一个用于刻画多语言软件的工具包
FSE Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering2022匹配度 2/10 · 论文关注多语言软件的漏洞修复提交分类与语言接口机制识别,属于软件工程与程序分析领域,但未涉及用户核心关注的机器学习系统、Agentic程序或LLM与静态分析的交叉,也无浮点或贝叶斯程序分析内容。引用 15 inbox
Wen Li, Li Li, Haipeng Cai
crossref: 2026-09-12

AI 速览

一句话:提出 PolyFax 工具包,支持从 GitHub 收集多语言软件数据集,并提供漏洞修复提交分类与语言接口机制识别/分类工具。

当今软件系统大多由多种语言开发,即多语言软件,但支持理解和保障此类系统的工具仍然稀缺。为促进多语言软件工程研究,本文提出 PolyFax 工具包,提供从 GitHub 自动收集数据集的能力,并集成两个分析工具:漏洞修复提交分类工具 VCC 和语言接口机制识别/分类工具 LIC。VCC 可基于版本历史评估给定多语言项目的漏洞倾向,LIC 则用于剖析多语言系统构建中最重要的语言交互方面。作者将 PolyFax 应用于 7,113 个多语言项目和 1,260 万次提交,结果表明其在研究多语言软件时具有可观的效率与准确度,具备实际有用性。

问题
多语言软件系统普遍存在,但用于理解和保障这类系统的工具支持很少。
方法
提出 PolyFax 工具包,支持从 GitHub 自动收集多语言软件数据集,并提供 VCC 与 LIC 两个分析工具,分别用于漏洞修复提交分类和语言接口机制识别/分类。
评估
将 PolyFax 应用于 7,113 个多语言项目和 1,260 万次提交,展示了其在多语言软件研究中的效率与准确度。
原文摘要(Abstract)

Today’s software systems are mostly developed in multiple languages (i.e., multi-language software), yet tool support for understanding and assuring these systems is rare. To facilitate future research on multi-language software engineering, this paper presents PolyFax, a toolkit that offers automated means for dataset collection from GitHub and two analysis utilities--a vulnerability-fixing commit categorization tool (VCC) and a language interfacing mechanism identification/categorization tool (LIC). The VCC tool immediately assists with assessing the vulnerability proneness of a given multi-language project based on its version histories, while the LIC tool enables dissection of the most important aspect of the construction of multi-language systems. Application of PolyFax to 7,113 multi-language projects with 12.6 million commits showed its practical usefulness in terms of promising efficiency and accuracy for studying multi-language software.

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BibTeX
@inproceedings{paperbot7019,
  title = {PolyFax: a toolkit for characterizing multi-language software},
  author = {Wen Li and Li Li and Haipeng Cai},
  booktitle = {FSE},
  year = {2022},
  doi = {10.1145/3540250.3558925}
}