ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems

2026-06-19

AI Summary

This work addresses the lack of a systematic hierarchical taxonomy for GitHub repositories, where existing tag-based mechanisms are flat, inconsistent, and sparsely populated. The authors propose the first end-to-end framework for automatically generating a hierarchical classification of repositories by integrating knowledge from large language models with empirical repository distributions. Their approach employs a multi-agent architecture—comprising designer agents that construct taxonomic dimensions and classifier agents that assign projects—augmented with an iterative self-correction mechanism and a novel hierarchical path evaluation strategy. Evaluated on a benchmark of 2,001 repositories, the method achieves a Taxonomy Quality Factor (TQF) of 83.13%, outperforming the best baseline by 15 percentage points. In downstream tasks, it attains 85.71% precision at rank 1 for alternative discovery, surpassing human-curated lists and substantially improving retrieval efficiency, while also uncovering evolutionary trends in domains such as AI and machine learning.

Abstract

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/

Problem

• hierarchical taxonomy
• software repositories
• GitHub Topics
• open-source ecosystem
• classification

Innovation

• hierarchical taxonomy
• agentic framework
• self-corrective refinement
• software ecosystem analysis
• LLM-augmented classification