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One of the major sources of failures in medical device systems is human error. Nurses, who often lack extensive technical background, misunderstand the device interface and misinterpret instructions given by the device. Safety assurance for medical devices is impossible without making them more robust to human error. An important direction of research in this project is establishing whether operation of the device is consistent with the mental model that a typical user of the device has. The technical approach is to consider mental models represented as finite state machines with transitions labeled by user interface elements of the device. Once such a model is constructed, the verification problem is to determine, whether the device operation is consistent with the model. Research concentrates on two approaches to determine consistency. One uses model-based testing, where tests are generated from the model and applied to the device. A more ambitious direction is to automatically extract an abstraction of the device operation from the source code of the embedded device software. Then, consistency can be established using well-known notions of state machine equivalence and refinement.

A feasibility study concentrates on a handheld point-of-injury data collection device, developed by TATRC, the U.S. Army medical research center. The project team, in collaboration with device developers, is constructing the mental model of the device user and is developing the means of model extraction based on the static analysis of the source code.

  • Model-Based Testing of GUI-Driven Applications, by Vivien Chinnapongse, Insup Lee, Oleg Sokolsky, Shaohui Wang, and Paul L. Jones, The Seventh IFIP Workshop on Software Technologies for Future Embedded and Ubiquitous Systems (SEUS 2009), Newport Beach, CA, LNCS 5860, pp. 203-214, November 16-19, 2009

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