Towards scalable compositional analysis by refactoring design models

Abstract

Automated finite-state verification techniques have matured considerably in the past several years, but state-space explosion remains an obstacle to their use. Theoretical lower bounds on complexity imply that all of the techniques that have been developed to avoid or mitigate state-space explosion depend on models that are "well-formed" in some way, and will usually fail for other models. This further implies that, when analysis is applied to models derived from designs or implementations of actual software systems, a model of the system "as built" is unlikely to be suitable for automated analysis. In particular, compositional, hierarchical analysis (where state-space explosion is avoided by simplifying models of subsystems at several levels of abstraction) depend on the modular structure of the model to be analyzed. We describe how as-built finite-state models can be refactored for compositional state-space analysis, applying a series of transformations to produce an equivalent model whose structure exhibits suitable modularity. The process is supported by a parser which can parse a subset of Promela syntax and transform Promela code into refactored state graphs.