Interpreting cloud computer vision pain-points: a mining study of stack overflow

Alex Cummaudo, Rajesh Vasa, Scott Barnett, John Grundy, Mohamed Abdelrazek

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

6 Citations (Scopus)

Abstract

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour-the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.

Original languageEnglish
Title of host publicationProceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering, ICSE 2020
EditorsJane Cleland-Huang, Darko Marinov
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1584-1596
Number of pages13
ISBN (Electronic)9781450371216
DOIs
Publication statusPublished - 2020
EventInternational Conference on Software Engineering 2020 - Virtual, Online, Korea, Republic of (South)
Duration: 27 Jun 202019 Jul 2020
Conference number: 42nd
https://dl.acm.org/doi/proceedings/10.1145/3377811 (Proceedings)
https://conf.researchr.org/home/icse-2020 (Website)

Conference

ConferenceInternational Conference on Software Engineering 2020
Abbreviated titleICSE 2020
Country/TerritoryKorea, Republic of (South)
CityVirtual, Online
Period27/06/2019/07/20
Internet address

Keywords

  • Computer vision
  • Documentation
  • Empirical study
  • Intelligent services
  • Pain points
  • Stack overflow

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