Investigating Bugs in AI-Infused Systems: Analysis and Proposed Taxonomy

Mohamad Kassab, Joanna Defranco, Phillip Laplante

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Testing for critical AI systems is non-trivial as these systems are prone to a new breed of sophisticated software defects. The admissibility of these systems and their fundamental social acceptance is tightly coupled with assuring whether the potential hazards to humans, animals, and property posed by the prospect defects can be minimized and limited to an acceptable level. In this work, we address the problem of assurance for critical AI systems by firstly, analyzing the nature of defects that occur in AI -infused systems in general and how to combat these within a testing strategy. Secondly, developing a focused taxon-omy of prospect defects in critical AI systems. This taxonomy enables the development of the non-critical proxy (i.e., stand-in) equivalent by reproducing defects with similar characteristics.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages365-370
Number of pages6
ISBN (Electronic)9781665476799
DOIs
StatePublished - 2022
Event33rd IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2022 - Virtual, Online, United States
Duration: Oct 31 2022Nov 3 2022

Publication series

NameProceedings - 2022 IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2022

Conference

Conference33rd IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2022
Country/TerritoryUnited States
CityVirtual, Online
Period10/31/2211/3/22

Keywords

  • AI
  • defects
  • software testing
  • taxonomy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Safety, Risk, Reliability and Quality

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