Abstract
Complex abstract data types are often used to facilitate creating concise models of the behavior of realistic systems. However, static analysis techniques that aim to optimize such models often consider variables of complex types as a single indivisible unit. The use of complex data types thus negatively affects the optimizations that can be performed. In this paper we revisit and extend a technique by Groote and Lisser that can be used to replace a single, complex variable by multiple variables of simpler data types, improving the effectiveness of other static analyzes. We describe the technique in the context of the process algebraic specification language mCRL2, and establish its correctness. We demonstrate using an implementation in the mCRL2 toolset that it sometimes reduces the size of the underlying state spaces, and it typically reduces the verification times when using symbolic model checking.
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Notes
- 1.
The definition using alternative case placement can be modified to take global variables into account in the same way.
- 2.
The SDs for the only cases where it exceeds 10% of the mean are: fourinarow4-3 standard 1.7, sla7 new.def: 0.3, tictactoe3-3 standard: 2.0, wms standard: 2.5, original: 2.2, new.def: 1.9.
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Acknowledgements
Michel Reniers and Frank Stappers previously described Groote and Lisser’s original definition of parameter unfolding in an unpublished note. Some of our notation is inspired by their note.
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Stramaglia, A., Keiren, J.J.A., Neele, T. (2023). Simplifying Process Parameters by Unfolding Algebraic Data Types. In: Ábrahám, E., Dubslaff, C., Tarifa, S.L.T. (eds) Theoretical Aspects of Computing – ICTAC 2023. ICTAC 2023. Lecture Notes in Computer Science, vol 14446. Springer, Cham. https://doi.org/10.1007/978-3-031-47963-2_24
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