Are Machine Learning Cloud APIs Used Correctly?

Chengcheng Wan, Shicheng Liu, Henry Hoffmann, Michael Maire, Shan Lu

DOI 10.1109/icse43902.2021.00024 dblp

Abstract

Machine learning (ML) cloud APIs enable developers to easily incorporate learning solutions into software systems. Unfortunately, ML APIs are challenging to use correctly and efficiently, given their unique semantics, data requirements, and accuracy-performance tradeoffs. Much prior work has studied how to develop ML APIs or ML cloud services, but not how open-source applications are using ML APIs. In this paper, we manually studied 360 representative open-source applications that use Google or AWS cloud-based ML APIs, and found 70% of these applications contain API misuses in their latest versions that degrade functional, performance, or economical quality of the software. We have generalized 8 anti-patterns based on our manual study and developed automated checkers that identify hundreds of more applications that contain ML API misuses.

BibTeX

@inproceedings{Wan-al:ICSE21,
  author    = {Chengcheng Wan and
               Shicheng Liu and
               Henry Hoffmann and
               Michael Maire and
               Shan Lu},
  title     = {Are Machine Learning Cloud {APIs} Used Correctly?},
  booktitle = {ICSE},
  pages     = {125--137},
  publisher = {{IEEE}},
  year      = {2021},
}

Are Machine Learning Cloud APIs Used Correctly?

Abstract

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