IAFinder: Identifying Potential Implicit Assumptions to Facilitate Validation in Medical Cyber-Physical System

摘要

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.