XFM: An incremental methodology for developing formal models

We present an agile formal methodology named eXtreme Formal Modeling (XFM), based on Extreme Programming (XP) concepts to construct abstract models from natural language specifications of complex systems. In particular, we focus on Prescriptive Formal Models (PFMs) that capture the specification of the system under design in a mathematically precise manner. Such models can be used as golden reference models for formal verification, test generation, coverage monitor generation, etc. This methodology for incrementally building PFMs works by adding user stories expressed as LTL formulae gleaned from the natural language specifications, one by one, into the model. XFM builds the models, retaining correctness with respect to incrementally added properties by regressively model-checking all the LTL properties captured theretofore in the model. We illustrate XFM with a graded set of examples consisting of a traffic light controller and a DLX pipeline. To make the regressive model-checking steps feasible with current model-checking tools, we need to control the model size increments at each subsequent step in the process. We therefore analyze the effects of ordering the LTL properties in XFM on the statespace growth rate of the model. We compare three different property-ordering methodologies: ad hoc ordering, property-based ordering, and predicate-based ordering. We experiment on the models of the ISA bus monitor and the arbitration phase of the Pentium Pro bus. We experimentally show and mathematically reason that the predicate-based ordering is the best among these orderings. Finally, we present a GUI-based toolbox that we implemented to build PFMs using XFM.