Introducing Ensemble Machine Learning Algorithms for Automatic Test Case Generation using Learning Based Testing

Context: Ensemble methods are powerful machine learning algorithms that combine multiple models to enhance prediction capabilities and reduce generalization errors. However, their potential to generate effective test cases for fault detection in a System Under Test (SUT) has not been extensively explored. Objective: This study aims to systematically investigate the combination of ensemble methods and base classifiers for model inference in a Learning Based Testing (LBT) algorithm to generate fault-detecting test cases for SUTs as a proof of concept. Method: We conduct a series of experiments on functions, generating effective test cases using different ensemble methods and classifier combinations for model inference in our proposed LBT method. We then compare the test suites based on their mutation score. Results: The results indicate that Boosting ensemble methods show overall better performance in generating effective test cases, and the proposed method is performing better than random generation. This analysis helps determine the appropriate ensemble methods for various types of functions. Conclusions: By incorporating ensemble methods into the LBT, this research contributes to the understanding of how to leverage ensemble methods for effective test case generation.