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Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystems

Hao Li, Cor-Paul Bezemer

TL;DR

The findings highlight key factors to consider for developers integrating bindings for ML libraries and open avenues for researchers to further investigate bindings in software package ecosystems.

Abstract

Open source machine learning (ML) libraries enable developers to integrate advanced ML functionality into their own applications. However, popular ML libraries, such as TensorFlow, are not available natively in all programming languages and software package ecosystems. Hence, developers who wish to use an ML library which is not available in their programming language or ecosystem of choice, may need to resort to using a so-called binding library (or binding). Bindings provide support across programming languages and package ecosystems for reusing a host library. For example, the Keras .NET binding provides support for the Keras library in the NuGet (.NET) ecosystem even though the Keras library was written in Python. In this paper, we collect 2,436 cross-ecosystem bindings for 546 ML libraries across 13 software package ecosystems by using an approach called BindFind, which can automatically identify bindings and link them to their host libraries. Furthermore, we conduct an in-depth study of 133 cross-ecosystem bindings and their development for 40 popular open source ML libraries. Our findings reveal that the majority of ML library bindings are maintained by the community, with npm being the most popular ecosystem for these bindings. Our study also indicates that most bindings cover only a limited range of the host library's releases, often experience considerable delays in supporting new releases, and have widespread technical lag. Our findings highlight key factors to consider for developers integrating bindings for ML libraries and open avenues for researchers to further investigate bindings in software package ecosystems.

Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystems

TL;DR

The findings highlight key factors to consider for developers integrating bindings for ML libraries and open avenues for researchers to further investigate bindings in software package ecosystems.

Abstract

Open source machine learning (ML) libraries enable developers to integrate advanced ML functionality into their own applications. However, popular ML libraries, such as TensorFlow, are not available natively in all programming languages and software package ecosystems. Hence, developers who wish to use an ML library which is not available in their programming language or ecosystem of choice, may need to resort to using a so-called binding library (or binding). Bindings provide support across programming languages and package ecosystems for reusing a host library. For example, the Keras .NET binding provides support for the Keras library in the NuGet (.NET) ecosystem even though the Keras library was written in Python. In this paper, we collect 2,436 cross-ecosystem bindings for 546 ML libraries across 13 software package ecosystems by using an approach called BindFind, which can automatically identify bindings and link them to their host libraries. Furthermore, we conduct an in-depth study of 133 cross-ecosystem bindings and their development for 40 popular open source ML libraries. Our findings reveal that the majority of ML library bindings are maintained by the community, with npm being the most popular ecosystem for these bindings. Our study also indicates that most bindings cover only a limited range of the host library's releases, often experience considerable delays in supporting new releases, and have widespread technical lag. Our findings highlight key factors to consider for developers integrating bindings for ML libraries and open avenues for researchers to further investigate bindings in software package ecosystems.
Paper Structure (32 sections, 6 equations, 9 figures, 3 tables)

This paper contains 32 sections, 6 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Paper Organization.
  3. Background
  4. Software Package Ecosystems
  5. Cross-Ecosystem Bindings for ML Libraries
  6. Related work
  7. Empirical Studies of ML Libraries
  8. Software Ecosystems
  9. Foreign Function Interfaces
  10. Methodology
  11. Collecting Open Source ML Repositories
  12. Collecting Bindings for Open Source ML Libraries
  13. Binding classification and host name extraction
  14. Studied models.
  15. Data preparation.
  16. ...and 17 more sections

Figures (9)

  • Figure 1: Overview of our methodology.
  • Figure 2: The model structure of BindFind for binding classification and host name extraction, illustrated using an example.
  • Figure 3: The distribution of the number of software package ecosystems supported by ML libraries with bindings.
  • Figure 4: Combinations of software package ecosystems in which ML libraries with bindings are available. The elements represent the number of libraries that can be found in both ecosystems (i.e., ecosystems in the row and column).
  • Figure 5: The process of identifying which version of the host library is supported by a specific binding version.
  • ...and 4 more figures