Empirical Study of Correlation between Mutation Score and Model Inference Based Test Suite Adequacy Assessment

In this paper we investigate a method for test suite evaluation that is based on an inferred model from the test suite. The idea is to use the similarity between the inferred model and the system under test as a measure of test suite adequacy, which is the ability of a test suite to expose errors in the system under test. We define similarity using the root mean squared error computed from the differences of the system under test output and the model output for certain inputs not used for model inference. In the paper we introduce the approach and provide results of an experimental evaluation where we compare the similarity with the mutation score. We used the Pearson Correlation coefficient to calculate whether a linear correlation between mutation score and root mean squared error exists. As a result we obtain that in certain cases the computed similarity strongly correlates with the mutation score.