An ensemble meta-estimator to predict source code testability
This work addresses software engineering challenges for developers by providing a predictive model to estimate testability, though it is incremental as it builds on existing metrics and methods.
The paper tackles predicting source code testability by developing a new equation based on test suite size and coverage, labeling 23,000 Java classes, and using regression models to achieve an R2 of 0.68 and a mean squared error of 0.03, with automated refactoring improving testability by an average of 86.87%.
Unlike most other software quality attributes, testability cannot be evaluated solely based on the characteristics of the source code. The effectiveness of the test suite and the budget assigned to the test highly impact the testability of the code under test. The size of a test suite determines the test effort and cost, while the coverage measure indicates the test effectiveness. Therefore, testability can be measured based on the coverage and number of test cases provided by a test suite, considering the test budget. This paper offers a new equation to estimate testability regarding the size and coverage of a given test suite. The equation has been used to label 23,000 classes belonging to 110 Java projects with their testability measure. The labeled classes were vectorized using 262 metrics. The labeled vectors were fed into a family of supervised machine learning algorithms, regression, to predict testability in terms of the source code metrics. Regression models predicted testability with an R2 of 0.68 and a mean squared error of 0.03, suitable in practice. Fifteen software metrics highly affecting testability prediction were identified using a feature importance analysis technique on the learned model. The proposed models have improved mean absolute error by 38% due to utilizing new criteria, metrics, and data compared with the relevant study on predicting branch coverage as a test criterion. As an application of testability prediction, it is demonstrated that automated refactoring of 42 smelly Java classes targeted at improving the 15 influential software metrics could elevate their testability by an average of 86.87%.