Therefore, errors in a system should be minimalized. There are some
techniques to do that. One of them is called model checking.
Model checking is used to prove that a system satisfies some certain
properties. If the system doesn't satisfy the properties, counterexample will be
produced. Then, the counterexample will be analyzed and used as the basis for
correcting the system.
Counterexample size will affect analysis process and process to correct the
system. Large counterexamples will make those processes more complicated. On
the other hand, small counterexamples will make them easier to do. So, model
checking process should compute small counterexample.
SPIN is a software that can be used to perform model checking. SPIN will
verify a system model and a certain property. If the property is violated by the
system model, SPIN will produce a counterexample. SPIN uses depth-first search
algorithm to compute counterexamples. In this research, we use another method,
based on Gastin and Moro's (2007) research, to generate minimal counterexample
for SPIN. We use breadth-first search algorithm that has already existed in SPIN's
source code to generate minimal counterexample. Counterexamples addressed in
this research are acceptance cycle.
The result of this research shows that the modified SPIN can generate
equal or smaller counterexamples than the unmodified one.