Comprehending the degree to which software components support testing is important to accurately schedule testing activities, train developers, and plan effective refactoring actions. Software testability estimates such property by relating code characteristics to the test effort. The main studies of testability reported in the literature investigate the relation between class metrics and test effort in terms of the size and complexity of the associated test suites. They report a moderate correlation of some class metrics to test-effort metrics, but suffer from two main limitations: (i) the results hardly generalize due to the small empirical evidence (datasets with no more than eight software projects); and (ii) mostly ignore the quality of the tests. However, considering the quality of the tests is important. Indeed, a class may have a low test effort because the associated tests are of poor quality, and not because the class is easier to test. In this paper, we propose an approach to measure testability that normalizes the test effort with respect to the test quality, which we quantify in terms of code coverage and mutation score. We present the results of a set of experiments on a dataset of 9,861 Java classes, belonging to 1,186 open source projects, with around 1.5 million of lines of code overall. The results confirm that normalizing the test effort with respect to the test quality largely improves the correlation between class metrics and the test effort. Better correlations result in better prediction power and thus better prediction of the test effort.