Abstract
In most current model checking based test generation approaches, property checking involves only one property at a time, and the checking of different properties are totally independent. This could be extremely time-consuming, since complex designs generally have a large set of properties that needs to be checked. This chapter presents a framework that can efficiently reduce the overall test generation time by exploiting the similarity among different properties. It presents various clustering strategies that can cluster similar properties together to enable learning sharing. In addition, this chapter investigates the conflict clause based learning that can be reused across properties to drastically reduce the overall test generation time.
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Notes
- 1.
This chapter uses three different types of graphs for three different purposes. The graph model of the design (or design graph in short) is used to model the design. The implication graph is used to store the dependence of variable assignments that is used for conflict analysis. The property graph models the similarity between properties and used for clustering.
- 2.
In a graph model, a local variable is defined locally inside a node whereas the scope of a global variable is valid across nodes.
- 3.
Clustering time using structural similarity is negligible and not shown in the table.
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Chen, M., Qin, X., Koo, HM., Mishra, P. (2013). Property Clustering and Learning Techniques. In: System-Level Validation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1359-2_5
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DOI: https://doi.org/10.1007/978-1-4614-1359-2_5
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