Source-Level Transformations for Improved Formal Verification

A major obstacle to widespread acceptance of formal verification is the difficulty in using the tools effectively. Although learning the basic syntax and operation of a formal verification tool may be easy, expert users are often able to accomplish a verification task while a novice user encounters time-out or space-out attempting the same task. In this paper, we assert that often a novice user will model a system in a different manner — semantically equivalent, but less efficient for the verification tool — than an expert user would, that some of these inefficient modeling choices can be easily detected at the source-code level, and that a robust verification tool should identify these inefficiencies and optimize them, thereby helping to close the gap between novice and expert users. To test our hypothesis, we propose some possible optimizations for the Murφ verification system, implement the simplest of these, and compare the results on a variety of examples written by both experts and novices (the Murφ distribution examples, a set of cache coherence protocol models, and a portion of the IEEE 1394 Firewire protocol). The results support our assertion — a nontrivial fraction of the Murφ models written by novice users were significantly accelerated by the very simple optimization. Our findings strongly support further research in this area.