Approximate Probabilistic Model Checking for Programs

In this paper we deal with the problem of applying model checking to real programs. We verify a program without constructing the whole transition system using a technique based on Monte-Carlo sampling, also called “approximate model checking”. This technique combines model checking and randomized approximation. Thus, it avoids the socalled state space explosion phenomenon. We propose a prototype implementation that works directly on C source code. It means that, contrary to others approaches, we do not need to use a specific language nor specific data structures in order to describe the system we wish to verify. Finally, we present experimental results that show the effectiveness of the approach applied to finding bugs in real programs.