  Evaluating Software testability can assist software managers in optimizing
testing budgets and identifying opportunities for refactoring. In this paper,
we abandon the traditional approach of pursuing testability measurements based
on the correlation between software metrics and test characteristics observed
on past projects, e.g., the size, the organization or the code coverage of the
test cases. We propose a radically new approach that exploits automatic test
generation and mutation analysis to quantify the amount of evidence about the
relative hardness of identifying effective test cases. We introduce two novel
evidence-based testability metrics, describe a prototype to compute them, and
discuss initial findings on whether our measurements can reflect actual
testability issues.
