Unveiling the Mystery of API Evolution in Deep Learning Frameworks: A Case Study of Tensorflow 2
Unveiling the Mystery of API Evolution in Deep Learning Frameworks: A Case Study of Tensorflow 2
复制标题
揭开深度学习框架API演变之谜：以Tensorflow 2为例
DOI：
10.1109/icse-seip52600.2021.00033
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
["Zejun Zhang
中科院分区：
文献类型：
--
作者：
["Zejun Zhang
API开发人员一直在努力发展API，以提供更简单，功能强大，强大的API库。尽管已经研究了用于多个领域的API进化，例如Web和Android开发，但尚未研究深度学习框架的API进化。目前尚不清楚API如何以及为什么在深度学习框架中发展，但是这些API在行业中越来越严重。为了填补这一空白，我们对Tensorflow 2的API演变进行了大规模和深入研究，该研究目前是最流行的深度学习框架。我们首先通过挖掘多个版本的TensorFlow 2的API文档来提取6,329个API更改，并将API更改为Tensorflow 2框架上的功能类别，以分析其API进化趋势。然后，我们通过参考多个信息源（例如API文档，Consits和StackOverFlow）来研究API更改的关键原因。最后，我们将非深度学习项目中的API演变与Tensorflow 2的API演变进行了比较，并确定了对用户，研究人员和API开发人员的一些关键含义。
API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract 6,329 API changes by mining API documentation of Tensorflow 2 across multiple versions and mapping API changes into functional categories on the Tensorflow 2 framework to analyze their API evolution trends. We then investigate the key reasons for API changes by referring to multiple information sources, e.g., API documentation, commits and StackOverflow. Finally, we compare API evolution in non-deep learning projects to that of Tensorflow 2, and identify some key implications for users, researchers, and API developers.
DOI：
10.1109/issre.2019.00020
发表时间：
2019-10
期刊：
2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)
影响因子：
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作者：
Tianyi Zhang;Cuiyun Gao;Lei Ma;Michael R. Lyu;Miryung Kim
通讯作者：
Tianyi Zhang;Cuiyun Gao;Lei Ma;Michael R. Lyu;Miryung Kim