构建首个开源软件生态的层级分类体系，自动识别项目归属。
ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems
- 用大模型结合真实项目分布，分步迭代生成合理分类维度。
- 在5万+仓库上达到83.13%分类质量，优于现有方法15个百分点。
- 可发现库向AI应用转型等深层趋势，适合开发者与研究者使用。
GitHub 上的开源生态缺乏系统化的软件仓库层级分类体系。当前主流的 GitHub Topics 是扁平、不一致的，仅覆盖67%的项目。本文提出 ATLAS，首个能自动构建软件仓库层级分类体系并端到端分类项目的框架。通过融合大模型全局知识与真实仓库分布，ATLAS 提出有意义的分割维度，并通过迭代修正失败的划分。设计代理（Designer Agent）提出分割维度，分类代理（Classifier Agent）分配仓库；自校正优化循环利用分类失败数据，以递进策略驱动维度修订。我们在54,387个GitHub仓库上评估了ATLAS，对比六种涵盖四种范式、两个下游任务、三种模型家族的基线。在2,001个分层基准上，ATLAS 达到83.13%的分类质量分数（TQF），领先最佳基线15个百分点（在完整5.4万语料中近似为73.0%，差距主要由路径粒度的全有或全无评分机制导致，非分类准确率不足）。它是唯一同时实现高结构质量和高实用性的方法。在下游任务中，ATLAS 实现替代发现的P@1=85.71%，超越人工精选列表（62.34%），并在仓库检索中取得最高P@1。该分类体系揭示了难以从扁平标签或相似性方法获取的生态结构趋势：从库向AI/ML应用的转变（现61%的新社区采纳项目属于此类）仅通过基于类型的层级分类才可见。交互式分类探索器见 https://atlas-taxonomy.netlify.app/
原文摘要 · Abstract (English)
The open-source ecosystem on GitHub lacks a systematic hierarchical taxonomy of software repositories. GitHub Topics, the dominant organizational mechanism, is flat, inconsistent, and covers only 67% of projects. We present ATLAS, the first framework that automatically constructs a hierarchical taxonomy for software repositories and classifies projects into it end-to-end. By combining LLM global knowledge with real repository distributions, ATLAS proposes meaningful splitting dimensions and iteratively corrects those that fail to accommodate real projects. A Designer Agent proposes splitting dimensions while a Classifier Agent assigns repositories; a self-corrective refinement loop uses classification failures to drive dimension revision through escalating strategies. We evaluate ATLAS on 54,387 GitHub repositories against six baselines spanning four paradigms, two downstream tasks, and three model families. On a stratified 2,001-repository benchmark, ATLAS achieves a Taxonomy Quality F-score (TQF) of 83.13%, outperforming the best baseline by 15 percentage points (on the full 54k corpus the approximate TQF is 73.0%, a gap driven by Path Granularity's all-or-nothing scoring on longer paths rather than lower classification accuracy). It is the only method to simultaneously achieve high structural quality and high practical applicability. On downstream tasks, ATLAS enables alternative discovery with P@1 = 85.71%, surpassing even human-curated lists (62.34%), and achieves the highest P@1 for repository retrieval. The taxonomy further reveals structural ecosystem trends that are difficult to obtain from flat tags or similarity methods: the shift from libraries to AI/ML applications (now 61% of newly community-adopted projects) becomes visible only through hierarchical, type-based categorization. An interactive taxonomy explorer is available at https://atlas-taxonomy.netlify.app/
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