Diversity in Search-Based Unit Test Suite Generation

Abstract

Search-based unit test generation is often based on evolutionary algorithms. Lack of diversity in the population of an evolutionary algorithm may lead to premature convergence at local optima, which would negatively affect the code coverage in test suite generation. While methods to improve population diversity are well-studied in the literature on genetic algorithms (GAs), little attention has been paid to diversity in search-based unit test generation so far. The aim of our research is to study the effects of population diversity on search-based unit test generation by applying different diversity maintenance and control techniques. As a first step towards understanding the influence of population diversity on the test generation, we adapt diversity measurements based on phenotypic and genotypic representation to the search space of unit test suites.