Abstract
This paper presents an iterative framework based on over-approximation and under-approximation for traditional bounded model checking (BMC). A novel feature of our approach is the approximations are defined based on “implication” instead of “simulation”. As a common partial order relation of logic formulas, implication is suitable for the satisfiability checking of BMC for debugging. Our approach could generate the implication-based approximations efficiently with necessary accuracy, thus it potentially enables BMC to go deeper and the output counterexamples with fewer variables are easier to understand. An experiment on a suite of Petri nets shows the effectiveness of implication-based approximating BMC.
This work was supported in part by the National Natural Science Foundation of China (60425206, 60373066, 60403016), Natural Science Foundation of Jiangsu Province (BK2005060), High Technology Research Project of Jiangsu Province (BG2005032), Excellent Talent Foundation on Teaching and Research of Southeast University, and Open Foundation of State Key Laboratory of Software Engineering in Wuhan University.
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Chen, Z., Tao, Z., Xu, B., Wang, L. (2007). Implication-Based Approximating Bounded Model Checking. In: Arbab, F., Sirjani, M. (eds) International Symposium on Fundamentals of Software Engineering. FSEN 2007. Lecture Notes in Computer Science, vol 4767. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75698-9_23
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DOI: https://doi.org/10.1007/978-3-540-75698-9_23
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