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5 个仓库

Awesome GitHub RepositoriesPatch Parsing

Utilities for converting serialized text diffs back into machine-operable patch objects.

Distinct from Patch Generators: Focuses on the parsing of patch strings rather than the generation of the patch

Explore 5 awesome GitHub repositories matching software engineering & architecture · Patch Parsing. Refine with filters or upvote what's useful.

Awesome Patch Parsing GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • evomap/evolverEvoMap 的头像

    EvoMap/evolver

    8,744在 GitHub 上查看↗

    Evolver is a self-evolving AI agent framework that uses gene expression programming to autonomously improve agent behaviors through a continuous five-step loop of scanning, selecting, mutating, validating, and solidifying. It functions as an auditable evolution system that records every mutation and selection step, and can translate natural-language problems into executable Python code for automated grading and evaluation. The framework distinguishes itself through a distributed architecture that enables multiple agents to collaborate and share learned experiences across a network. It operate

    Captures runtime execution failures and iteratively applies minimal patches to produce working submissions.

    JavaScripta2aagent-evolutionagent-framework
    在 GitHub 上查看↗8,744
  • google/diff-match-patchgoogle 的头像

    google/diff-match-patch

    8,065在 GitHub 上查看↗

    This is a text diffing and patching library used for computing differences between text blocks, calculating edit distances, and applying patches to synchronize document versions. It includes a fuzzy text matching engine to locate strings by balancing accuracy with location, and a Levenshtein distance calculator to measure the number of character insertions, deletions, and substitutions between two strings. The library features a semantic diff optimizer that refines raw text differences to align with human-readable word and phrase boundaries. It provides utilities for generating and parsing se

    Converts textual diff formats back into machine-readable patches to apply changes to documents.

    Pythondiffdifferencematch
    在 GitHub 上查看↗8,065
  • kodezi/chronosKodezi 的头像

    Kodezi/Chronos

    4,919在 GitHub 上查看↗

    Chronos 是一个基于 LLM 的软件工程智能体和仓库级调试模型,专为自动修复 Bug 而设计。该系统作为自动化 Bug 修复系统,能够定位缺陷、分析根本原因并实施经过验证的多文件补丁。 该项目的特点是采用图引导检索引擎,利用持久化内存图来导航大型仓库中的调用关系和数据流。它采用持久化调试工作流,索引提交历史和日志以识别模式,从而在迭代调试周期中避免重复犯错。 该系统通过因果追踪定位和跨文件逻辑 Bug 追踪来涵盖根本原因分析。它进一步通过合成补丁并在迭代沙箱环境中进行验证,提供自动代码修复功能。仓库智能通过多跳上下文检索以及日志和追踪的多源摄取来处理。 该系统提供推理步骤透明度,并生成自动调试文档,包括风险评估和对每个已应用修复的理由说明。

    Creates complex code fixes and supporting tests across a large-scale repository using a specialized debugging model.

    Javaartificial-intelligenceautonomous-debuggingbenchmark
    在 GitHub 上查看↗4,919
  • gptme/gptmegptme 的头像

    gptme/gptme

    4,343在 GitHub 上查看↗

    gptme 是一个自主 AI 代理服务器和框架,专为本地系统自动化、软件开发和代码执行而设计。它作为本地执行引擎运行,使语言模型能够运行 Shell 命令、修改本地文件并与操作系统交互。 该项目充当 Model Context Protocol 客户端,与外部服务器集成,以使用标准化工具和数据源扩展代理功能。它具有提供程序无关的路由系统,可编排跨多个专有云 API 和本地 AI 后端的任务。 该系统包括用于无头浏览器自动化、视觉内容分析和用于映射代码库的符号代码分析的功能。为确保安全,它实现了人机协作(human-in-the-loop)护栏,在执行敏感系统更改或最终确定文件补丁之前需要用户确认。 该应用程序可以作为独立桌面二进制文件或通过 Docker 容器化进行部署。

    Updates local files using a combination of full overwrites and incremental patching for precise content editing.

    Python
    在 GitHub 上查看↗4,343
  • erikbjare/gptmeErikBjare 的头像

    ErikBjare/gptme

    4,334在 GitHub 上查看↗

    gptme 是一个多智能体编排平台,专为自主软件工程、终端 AI 集成和 RAG 增强的代码导航而设计。它支持部署持久化智能体和专用子智能体,以分解复杂任务并执行并行技术工作流。 该系统通过结合用于控制桌面应用的基于视觉的 GUI 自动化和用于目标源代码修改的外科手术式补丁机制,展现出其独特之处。它利用基于 Git 的内存管理来维护智能体身份、经验和工作区状态的版本化历史。 其更广泛的能力涵盖跨本地和云 AI 后端的与提供商无关的模型路由、用于本地上下文的语义检索,以及集成模型上下文协议(MCP)以动态加载外部工具。该项目还包括一个用于自动化调试、重构和 GitHub 仓库管理的综合软件工程套件。 该平台可通过 Docker 容器作为自托管服务器部署,具有基于 Web 的聊天界面和容器化桌面渲染功能。

    Performs large-scale or scattered file modifications using a specialized high-speed patching model.

    Python
    在 GitHub 上查看↗4,334
  1. Home
  2. Software Engineering & Architecture
  3. Code Patching Engines
  4. Patch Generators
  5. Patch Parsing

探索子标签

  • Patch Repair2 个子标签Fixing malformed patches by adjusting hunk headers and removing noise. **Distinct from Patch Parsing:** Focuses on correcting model-generated patch errors rather than just parsing serialized diffs into objects.