Abstract
The test generation performance using SAT-based BMC mainly depends on the efficiency of SAT search heuristics which can find satisfying assignments quickly. Since similar properties and SAT instances describe correlated functional scenarios, their significant overlap on the counterexample assignments can be used as learnings for the SAT search. This chapter explores such learnings within a SAT stance and among similar SAT instances. The proposed intra- and inter-property learnings based on decision ordering heuristics and conflict clause forwarding techniques can be used to improve the overall test generation time for a single property as well as a cluster of similar properties.
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Notes
- 1.
A CNF SAT instance can be viewed as a union of a set of segments where each segment consists of a set of CNF clauses.
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Chen, M., Qin, X., Koo, HM., Mishra, P. (2013). Decision Ordering Based Learning Techniques. In: System-Level Validation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1359-2_6
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