Unveiling the mystery of API evolution in Deep Learning frameworks: case study of Tensorflow 2

Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, John Grundy

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/ACM 43rd International Conference on Software Engineering
Subtitle of host publicationSoftware Engineering in Practice, ICSE-SEIP 2021
EditorsSigrid Eldh, Davide Falessi
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9780738146690
ISBN (Print)9781665438698
Publication statusPublished - 2021
EventInternational Conference on Software Engineering 2021: Software Engineering in Practice - Online, Madrid, Spain
Duration: 25 May 202128 May 2021
Conference number: 43rd
https://ieeexplore-ieee-org.ezproxy.lib.monash.edu.au/xpl/conhome/9401806/proceeding (Proceedings)

Publication series

NameProceedings - International Conference on Software Engineering
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
ISSN (Print)0270-5257


ConferenceInternational Conference on Software Engineering 2021
Abbreviated titleICSE-SEIP 2021
OtherTrack within the International Conference on Software Engineering
Internet address


  • Api documentation
  • Api evolution
  • Deep learning
  • Tensorflow 2

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