  Synchronization is the Achilles heel of concurrent programs. Synchronization
requirement is often used to ensure that the execution of the concurrent
program can be serialized. Without synchronization requirement, a program
suffers from consistency violations. Recently, it was shown that if programs
are designed to tolerate such consistency violation faults (\cvf{s}) then one
can obtain substantial performance gain. Previous efforts to analyze the effect
of \cvf-tolerance are limited to run-time analysis of the program to determine
if tolerating \cvf{s} can improve the performance. Such run-time analysis is
very expensive and provides limited insight.
  In this work, we consider the question, `Can static analysis of the program
predict the benefit of \cvf-tolerance?' We find that the answer to this
question is affirmative. Specifically, we use static analysis to evaluate the
cost of a \cvf and demonstrate that it can be used to predict the benefit of
\cvf-tolerance. We also find that when faced with a large state space, partial
analysis of the state space (via sampling) also provides the required
information to predict the benefit of \cvf-tolerance. Furthermore, we observe
that the \cvf-cost distribution is exponential in nature, i.e., the probability
that a \cvf has a cost of $c$ is $A.B^{-c}$, where $A$ and $B$ are constants,
i.e., most \cvf{s} cause no/low perturbation whereas a small number of \cvf{s}
cause a large perturbation. This opens up new aveneus to evaluate the benefit
of \cvf-tolerance.
