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Extracting enhanced artificial intelligence model metadata from software repositories

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Abstract

While artificial intelligence (AI) models have improved at understanding large-scale data, understanding AI models themselves at any scale is difficult. For example, even two models that implement the same network architecture may differ in frameworks, datasets, or even domains. Furthermore, attempting to use either model often requires much manual effort to understand it. As software engineering and AI development share many of the same languages and tools, techniques in mining software repositories should enable more scalable insights into AI models and AI development. However, much of the relevant metadata around models are not easily extractable. This paper (an extension of our MSR 2020 paper) presents a library called AIMMX for AI Model Metadata eXtraction from software repositories into enhanced metadata that conforms to a flexible metadata schema. We evaluated AIMMX against 7,998 open-source models from three sources: model zoos, arXiv AI papers, and state-of-the-art AI papers. We also explored how AIMMX can enable studies and tools to advance engineering support for AI development. As preliminary examples, we present an exploratory analysis for data and method reproducibility over the models in the evaluation dataset and a catalog tool for discovering and managing models. We also demonstrate the flexibility of extracted metadata by using the evaluation dataset in an existing natural language processing (NLP) analysis platform to identify trends in the dataset. Overall, we hope AIMMX fosters research towards better AI development.

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Notes

  1. At the time of publishing, their data are available under the CC BY-SA license.

  2. https://cloud.ibm.com/catalog/services/natural-language-understanding

  3. https://github.com/IBM/github-crawler

  4. https://ibm.biz/ai-model-catalog-emse

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Authors and Affiliations

  1. IBM Research, Yorktown Heights, NY, USA

    Jason Tsay, Martin Hirzel, Avraham Shinnar & Todd Mummert

  2. IBM Research Brazil, São Paulo, Brazil

    Alan Braz

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  1. Jason Tsay
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  2. Alan Braz
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  3. Martin Hirzel
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  5. Todd Mummert
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Correspondence to Jason Tsay.

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Communicated by: Georgios Gousios, Sarah Nadi

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This article belongs to the Topical Collection: Mining Software Repositories (MSR)

Appendix A: List of Domains and Tasks Inferred

Appendix A: List of Domains and Tasks Inferred

  • Computer Vision

    • Face Detection

    • Face Verification

    • Image Classification

    • Image Denoising

    • Image Generation

    • Image-to-Image Translation

    • Object Detection

    • Object Localization

    • Person Re-Identification

    • Pose Estimation

    • Scene Text Detection

    • Semantic Segmentation

    • Visual Question Answering

    • Vision Other

  • Natural Language Processing

    • Dependency Parsing

    • Language Modelling

    • Machine Translation

    • Named Entity Recognition (NER)

    • Natural Language Inference

    • Part-Of-Speech Tagging

    • Question Answering

    • Sentiment Analysis

    • Text Classification

    • Text Generation

    • NLP Other

  • Other Domains

    • Graphs

    • Medical

    • Playing Games

    • Speech

    • Miscellaneous

  • Unknown

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Tsay, J., Braz, A., Hirzel, M. et al. Extracting enhanced artificial intelligence model metadata from software repositories. Empir Software Eng 27, 176 (2022). https://doi.org/10.1007/s10664-022-10206-6

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