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UACFinder: Mining Syntactic Carriers of Unspecified Assumptions in Medical Cyber-Physical System Design Models

Published: 12 March 2020 Publication History

Abstract

During the system development process, domain experts and developers often make assumptions about specifications and implementations. However, most of the assumptions being taken for granted by domain experts and developers are too tedious to be documented by them. When these unspecified assumptions are violated in an environment in which the system operates, failures can occur. According to the U.S. Food and Drug Administration (FDA) medical device recall database, medical device recalls caused by software failures are at an all-time high. One major cause of these recalls is violations of unspecified assumptions made in medical systems. Therefore, it is crucial to have tools to automatically identify such unspecified assumptions at an early stage of the systems development process to avoid fatal failures.
In this article, we present a tool called Unspecified Assumption Carrier Finder (UACFinder) that uses data mining techniques to automatically identify potential syntactic carriers of unspecified assumptions in system design models. The main idea of this tool is based on the observation we obtained from our earlier analysis of software failures in medical device recalls caused by unspecified assumptions. We observed that unspecified assumptions often exist in medical systems through syntactic carriers, such as constant variables, frequently read/updated variables, and frequently executed action sequences. Therefore, we develop the UACFinder to automatically find these potential unspecified assumption syntactic carriers rather than unspecified assumptions themselves. Once the UACFinder identifies the potential unspecified assumption syntactic carriers, domain experts and developers can validate whether these syntactic carriers indeed carry unspecified assumptions. We use a simplified cardiac arrest treatment scenario as a case study to evaluate the UACFinder in mining potential syntactic carriers of unspecified assumptions. In addition, we invite a medical doctor to validate unspecified assumptions carried by the mined syntactic carriers. The case study demonstrates that the UACFinder is effective in helping to identify potential unspecified assumptions from system design models.

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Published In

cover image ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems  Volume 4, Issue 3
Special Issue on User-Centric Security and Safety for CPS
July 2020
279 pages
ISSN:2378-962X
EISSN:2378-9638
DOI:10.1145/3388234
  • Editor:
  • Tei-Wei Kuo
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 March 2020
Accepted: 01 October 2019
Revised: 01 September 2019
Received: 01 May 2019
Published in TCPS Volume 4, Issue 3

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Author Tags

  1. Medical cyber-physical systems
  2. data mining
  3. statechart models
  4. syntactic carriers
  5. unspecified assumptions

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Cited By

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  • (2023)Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine LearningHealthcare10.3390/healthcare1103038411:3(384)Online publication date: 29-Jan-2023
  • (2023)Filtering Out High Noise Data for Distributed Deep Neural NetworksIEEE Transactions on Automation Science and Engineering10.1109/TASE.2022.320802720:1(101-111)Online publication date: Jan-2023
  • (2022)Confidence Composition for Monitors of Verification Assumptions2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS54341.2022.00007(1-12)Online publication date: May-2022
  • (2021)Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory StudyProceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering10.1145/3463274.3463333(139-148)Online publication date: 21-Jun-2021

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