MLCatchUp:自动更新Python中已弃用的机器学习api

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI:10.26226/morressier.613b5418842293c031b5b5cd

S. A. Haryono, Ferdian Thung, David Lo, J. Lawall, Lingxiao Jiang

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引用次数: 3

摘要

机器学习(ML)库越来越受欢迎，尤其是在Python编程语言中。建议使用这些库的最新版本，以确保最佳性能和安全性。当迁移到最新版本的机器学习库时，需要更新已弃用的api的用法，这是一个耗时的过程。在本文中，我们提出了MLCatchUp，这是一个自动API使用更新工具，用于使用Python编写的流行ML库中已弃用的API。MLCatchUp通过已弃用和更新的API签名之间的差异，自动推断迁移已弃用API的用法所需的转换。MLCatchUp以领域特定语言(DSL)的形式提供了可读的转换规则。我们使用267个真实Python代码的数据集来评估MLCatchUp，其中包含68个不同的已弃用api的551个用法，其中MLCatchUp达到了90.7%的准确率。MLCatchUp的视频演示可在https://youtu.be/5NjOPNt5iaA上获得。

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MLCatchUp: Automated Update of Deprecated Machine-Learning APIs in Python

Machine learning (ML) libraries are gaining vast popularity, especially in the Python programming language. Using the latest version of such libraries is recommended to ensure the best performance and security. When migrating to the latest version of a machine learning library, usages of deprecated APIs need to be updated, which is a time-consuming process. In this paper, we propose MLCatchUp, an automated API usage update tool for deprecated APIs of popular ML libraries written in Python. MLCatchUp automatically infers the required transformation to migrate usages of deprecated API through the differences between the deprecated and updated API signatures. MLCatchUp offers a readable transformation rule in the form of a domain specific language (DSL). We evaluate MLCatchUp using a dataset of 267 real-world Python code containing 551 usages of 68 distinct deprecated APIs, where MLCatchUp achieves 90.7% accuracy. A video demonstration of MLCatchUp is available at https://youtu.be/5NjOPNt5iaA.

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2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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