Evaluating Automatic Research Platforms via Open-Source Community Popularity Metrics: An Ecological Assessment Framework

Chenfeng Yu; Jingming Li

doi:10.62177/jaet.v3i3.1559

Authors

Chenfeng Yu Nanyang Normal University
Jingming Li Nanyang Normal University

DOI:

https://doi.org/10.62177/jaet.v3i3.1559

Keywords:

Automatic Research Platforms, Open-Source Community Evaluation, Altmetrics, Platform Ecosystem Assessment, Quantitative Scoring Model

Abstract

Automatic research platforms that leverage large language models (LLMs) and multi-agent frameworks to autonomously conduct scientific discovery have proliferated rapidly in open-source communities. Evaluating these platforms remains a challenge: traditional methods assess output quality with accuracy, logic, and completeness, yet these dimensions are subjective, difficult to standardize across heterogeneous platforms, and blind to long-term ecological vitality. This thesis proposes an ecological evaluation framework that relies exclusively on open-source community popularity metrics as the sole basis for platform assessment. We construct a multi-dimensional indicator system encompassing four dimensions of platform popularity, community activity, maintenance sustainability, and ecological expansion, and develop a unified quantitative scoring model grounded in GitHub metrics with stars, forks, issues, pull requests, contributors, and commit frequency and time-decayed heat scores. Empirical analysis covers 54 general-purpose and 112 science-oriented automatic research platforms, yielding comprehensive community-level rankings. Our results demonstrate that community popularity metrics effectively differentiate platforms by sustained engagement and long-term development potential, revealing an evaluation perspective that complements and, in some respects, surpasses traditional quality-centric assessments. The proposed framework provides actionable insights for platform selection by users, operational optimization by developers, and ecosystem analysis for researchers.

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