Improving Automatic Test Case Generation by predicting optimal Fitness Functions

Abstract

Recently, automating test suite generation is a problem that has drown attention in both industry and academia. One of the tools used to automatically generate test suites is EvoSuite, which is a state-of-the-art tool often used in research. It uses a genetic algorithm, which seeks to maximize certain coverage criteria, such as Branch Coverage or Exception Coverage. Previous research has investigated the possibility of combining multiple coverage criteria, but there is no single combination which performs best for all classes. This paper aims to investigate if it possible to predict when coverage criteria perform best, specifically Exception Coverage, according to the characteristics of the class under test. The paper shows that there is a significant difference between the performances of these coverage criteria. The paper also shows that this difference can be predicted using a Machine Learning model with an f1-score of 0,865, when performance was measured using Mutation Score. Lastly, an exploration is made into the characteristics of the Class-under-test which cause this.