IAFinder: Identifying potential implicit assumptions to facilitate validation in medical cyber-physical system

Zhicheng Fu, Zhao Wang, Chunhui Guo, Zhenyu Zhang, Shangping Ren, Lui Sha

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

Abstract

According to the U.S. Food and Drug Administration (FDA) medical device recall database, medical device recalls are at an all-time high. One of the major causes of the recalls is due to implicit assumptions of which either the medical device operating environment does not match, or the device operators are not aware of. In this paper, we present IAFinder (Implicit Assumption Finder), a tool that uses data mining techniques to automatically extract invariants from design models implemented with statecharts. By identifying invariants that are not explicitly specified in the design models, we are able to find implicit assumptions and better facilitate domain experts to validate them and make the validated implicit assumptions explicit. We use a cardiac arrest statechart model as a case study to illustrate the usage of IAFinder in identifying implicit assumptions.

Original languageEnglish (US)
Title of host publicationProceedings of the 55th Annual Design Automation Conference, DAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781450357005
DOIs
StatePublished - Jun 24 2018
Event55th Annual Design Automation Conference, DAC 2018 - San Francisco, United States
Duration: Jun 24 2018Jun 29 2018

Publication series

NameProceedings - Design Automation Conference
VolumePart F137710
ISSN (Print)0738-100X

Other

Other55th Annual Design Automation Conference, DAC 2018
CountryUnited States
CitySan Francisco
Period6/24/186/29/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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