paperbot · PL 论文追踪

全部论文 待读 收藏

P2 PyGGI 2.0: language independent genetic improvement framework

PyGGI 2.0：语言无关的遗传改进框架
FSE Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering2019匹配度 2/10 · 该论文聚焦基于遗传改进的程序修复框架，核心是XML中间表示与跨语言复用，未涉及用户重点关注的静态分析、LLM/Agent、浮点程序分析或贝叶斯程序分析。虽然属于PL与AI交叉中的程序修复方向，但与用户当前研究画像的核心问题距离较远。引用 27 inbox
Gabin An, Aymeric Blot, Justyna Petke, Shin Yoo
测试、调试与修复编译与代码生成
crossref: 2026-09-12

AI 速览

一句话：提出基于 XML 中间程序表示的 PyGGI 2.0，使遗传改进算子和算法可跨目标语言复用。

PyGGI 是面向遗传改进（GI）的研究工具，旨在通用易用。2.0 版核心是 XML 中间程序表示，用户可定义与语言无关的 GI 算子和算法，并复用于多种目标语言。作者通过两个案例研究评估：在 QuixBugs 基准上做自动程序修复（含 Python/Java 缺陷程序），以及复现 MiniSAT 求解器的运行时优化/程序特化工作。结果显示 PyGGI 2.0 能为此前所有 APR 工具未修复的缺陷生成补丁，并在 MiniSAT 上获得 14% 运行时提升，表明新版本的适用性与表达力。

问题
遗传改进工具通常与特定语言绑定，难以复用 GI 算子与算法，需要一种语言无关的表示与框架。
方法
PyGGI 2.0 引入 XML 中间程序表示，并在此基础上支持跨语言定义和复用 GI 算子和算法。
评估
在 QuixBugs 自动程序修复基准和 MiniSAT 运行时程序特化案例上评估；修复了已有工具未修复的缺陷，并取得 14% 运行时提升。
原文摘要（Abstract）

PyGGI is a research tool for Genetic Improvement (GI), that is designed to be versatile and easy to use. We present version 2.0 of PyGGI, the main feature of which is an XML-based intermediate program representation. It allows users to easily define GI operators and algorithms that can be reused with multiple target languages. Using the new version of PyGGI, we present two case studies. First, we conduct an Automated Program Repair (APR) experiment with the QuixBugs benchmark, one that contains defective programs in both Python and Java. Second, we replicate an existing work on runtime improvement through program specialisation for the MiniSAT satisfiability solver. PyGGI 2.0 was able to generate a patch for a bug not previously fixed by any APR tool. It was also able to achieve 14% runtime improvement in the case of MiniSAT. The presented results show the applicability and the expressiveness of the new version of PyGGI. A video of the tool demo is at: https://youtu.be/PxRUdlRDS40.

链接与引用

DOI 原文 · PDF（开放获取） ·

BibTeX 
@inproceedings{paperbot5050,
  title = {PyGGI 2.0: language independent genetic improvement framework},
  author = {Gabin An and Aymeric Blot and Justyna Petke and Shin Yoo},
  booktitle = {FSE},
  year = {2019},
  doi = {10.1145/3338906.3341184}
}