Symbolic Model Checking Visualization
The industrial deployment of formal verification technology that has not yet reached its maturity is extremely difficult. The more automated and therefore most widely used formal verification technology, symbolic model checking, has a severe problem of limited capacity. The capacity limitation reflects itself in long, and most importantly, unpredictable run time duration which results in low productivity.
In this paper, we demonstrate how we integrated techniques from the fields of algorithm animation, performance monitoring, and knowledge engineering in order to boost two major problem areas of model checking: the capacity limitation and low productivity. We have developed a prototype, called Palette, in order to demonstrate these concepts.
Palette uses visualization techniques to give insight to the execution of the symbolic model checking algorithms. Furthermore, it tracks the progress of the verification run and enables mid-course analysis of the status. Palette makes a step forward in estimation of the run time duration by predicting the amount of work done in some selected execution tasks, and informing the ones that are to be executed in the future. An additional important goal of Palette is to assist in building and automating the model checking usage methodology, and consequently to reduce the need for user expertise and intervention. In this aspect, Palette is a light-weight expert system for model checking. It can determine which algorithms are not efficient and reason on their failure. It can also advice on how to make a verification task complete successfully.
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- G. Kamhi, O. Weissberg, L. Fix, Z. Binyamini, Z. Shtadler. Automatic Datapath Extraction for Efficient Usage of HDDs. In Proceedings of the 9th International Conference on Computer-Aided Verification, CAV’97, Haifa, Israel, June 1997, LNCS 1254.Google Scholar
- K. L. McMillan. Fitting Formal Methods into the Design Cycle, In Proceedings of the 31st Design Automation Conference, June 1994.Google Scholar
- K. L. McMillan. Symbolic Model Checking, Kluwer Academic Publishers.Google Scholar
- R. E. Bryant. Graph-based Algorithms for Boolean Function Manipulation. IEEE Transactions on Computers, c-35(8), pp. 677–691, Aug. 1986Google Scholar
- I. Beer, S. Ben-David, C. Eisner, and Avner Landver. Rulebase: an industry-oriented formal verification tool. In Proceedings of the 33rd Design Automation Conference. IEEE Computer Society Press, June 1996.Google Scholar
- E.A. Feigenbaum. The Art of Artificial Intelligence: Themes and Case Studies in Knowledge Engineering, In Proceedings of IJCAI 5, 1977.Google Scholar
- Stefik, Aikins, Balzer, Benoit, Birnbaum, Hayes Roth, Sacerdoti. “The Organization of Expert Systems”, Artificial Intelligence, Vol. 18, pp. 135–173, Mar. 1982Google Scholar
- Sridharan, N. S., “Artificial Intelligence 11, Special Issue on Applications to the Sciences and Medicine”, 1978Google Scholar
- C. A. Kulikowski, “Artificial Intelligence Methods and Systems for Medical Consultation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Sept. 1980Google Scholar
- E. Rich, Artificial Intelligence. MC Graw Hill Series.Google Scholar
- M. H. Brown, J. Hershberger, “Color and Sound in Algorithm Animation”, IEEE Computer, December 1992.Google Scholar
- M. H. Brown, J. Hershberger, “Zeus: A System for Algorithm Animation”, In Proceedings of IEEE 1991 Workshop on Visual LanguagesGoogle Scholar
- “Tango: A Framework and System for Algorithm Animation”, IEEE Computer, Sept. 1990Google Scholar
- M. H. Brown, “Exploring Algorithms Using Balsa-II, IEEE Computer, May 1988Google Scholar
- R. S. Michalski, R. L. Chilausky, “Learning by Being Told and Learning from Examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis”, In “Policy Analysis and Information Systems”, Special Issue on Knowledge Acquisition and Induction, No. 2, 1980Google Scholar