1. Model Checking and Model-Based Design Bruce H. Krogh Carnegie Mellon University

2. IT Issues in the White Papers representation system integration component interfaces configuration quality-of-service data recording real-time constraints networked components security feature extraction heterogeneity dependability customizability privacy information fusion visualization robustness diagnostics model validation haptic interfacing real-time control usability etc.

3. IT "Solutions" in the White Papers information dynamics abstraction model checking plug and play code generation reference models middleware model-based design metamodeling architecture description languages user scenarios reinforcement learning model predictive control validation artifacts compositional reasoning proof carrying code open source information exchange formats hierarchical models coverage metrics assumption management UML hybrid systems wireless sensor networks etc.

4. IT "Solutions" in the White Papers information dynamics abstraction model checking plug and play code generation reference models middleware model-based design metamodling architecture description languages user scenarios reinforcement learning model predictive control validation artifacts compositional reasoning proof carrying code open source information exchange formats hierarchical models coverage metrics assumption management UML hybrid systems wireless sensor networks etc.

5. Model Checking FINITE-STATE SYSTEM PROPERTY TO VERIFY MODEL CHECKING PROGRAM PROPERTY IS TRUE OR A COUNTER EXAMPLE propagates sets of states, not individual trajectories

6. Model Checking Success Story – IC Design Initial verification problem –Check early if there are problems with the spec or with the idealized initial design System integration verif. problem –Check late for problems caused when ideal blocks become real circuits with unwanted but unavoidable behaviors

7. History of Model Checking from "Software technology maturation study: Model checking techniques and tools," V. Poladian, 2001.

8. History of Model Checking from "Software technology maturation study: Model checking techniques and tools," V. Poladian, 2001.

9. Model Checking for Medical Devices Possibilities –procedure verification –requirements analysis –fault mode coverage & correctness Barriers –formalizing requirements –modeling device environment (patients) –dealing with continuous phenomena –effective integration into the development process

10. Infusion Pump Experience

11. Infusion Pump Experience – Masters of Software Engineering Course: Models of Software Systems Objective: How to apply light-weight formal methods to complex software systems to requirements of an infusion pump Documents provided by the FDA and others Assignments allow a student to better understand –specification of real-world systems using various notations; –translation of informally-defined faults into checkable properties of a formal model; –tradeoffs in expressiveness between different forms of specification (pre-post condition models; relational models; process algebraic models) –the use of verification tools such as model checkers and theorem provers.

12. Model-Based Development Goal: Use models for requirements design verification test case generation modification documentation code generation run-time monitoring etc.

13. History of Model-Based Development 1970advent of computer-aided control system design (CACSD) tools 1980graphical development tools for system design and simulation 1990tools for automatic code generation 2000tool integration

14. Model-Based Development for Medical Devices Possibilities –high confidence software and systems –documentation for the life of the device –artifacts for device approval Barriers –capturing knowledge –data-based modeling –domain-relevant abstractions –overhead for a "secondary" activity (software isn't the point!)

15. White Paper 1: New alternative to model-based development

16. Model Checking and Model-Based Development: Lessons Learned real solutions must tailored to the domain stand-alone technologies are of limited value there must be champions of the technology in the industry we need nonproprietary test beds we need to be naively optimistic

17. White Paper 2: Proposed Open Experimental Platform