Losing confidence in quality: Unspoken evolution of computer vision services

Alex Cummaudo, Rajesh Vasa, John Grundy, Mohamed Abdelrazek, Andrew Cain

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

4 Citations (Scopus)

Abstract

Recent advances in artificial intelligence (AI) and machine learning (ML), such as computer vision, are now available as intelligent services and their accessibility and simplicity is compelling. Multiple vendors now offer this technology as cloud services and developers want to leverage these advances to provide value to end-users. However, there is no firm investigation into the maintenance and evolution risks arising from use of these intelligent services; in particular, their behavioural consistency and transparency of their functionality. We evaluated the responses of three different intelligent services (specifically computer vision) over 11 months using 3 different data sets, verifying responses against the respective documentation and assessing evolution risk. We found that there are: (1) inconsistencies in how these services behave; (2) evolution risk in the responses; and (3) a lack of clear communication that documents these risks and inconsistencies.We propose a set of recommendations to both developers and intelligent service providers to inform risk and assist maintainability.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Software Maintenance and Evolution, ICSME 2019
EditorsMiryung Kim, Árpád Beszédes
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages333-342
Number of pages10
ISBN (Electronic)9781728130941
ISBN (Print)9781728130958
DOIs
Publication statusPublished - 2019
EventIEEE International Conference on Software Maintenance and Evolution 2019 - Cleveland, United States of America
Duration: 30 Sep 20194 Oct 2019
Conference number: 35th
https://icsme2019.github.io/
https://ieeexplore.ieee.org/xpl/conhome/8910135/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Software Maintenance and Evolution 2019
Abbreviated titleICSME 2019
CountryUnited States of America
CityCleveland
Period30/09/194/10/19
Internet address

Keywords

  • computer vision
  • documentation
  • evolution risk
  • intelligent service
  • Machine learning
  • quality assurance

Cite this