Evolutionary Unit Testing Of Object-Oriented Software Using A Hybrid Evolutionary Algorithm

Abstract

Evolutionary algorithms have been successfully applied in the area of software testing. However, previous approaches in the area of object-oriented testing are limited in terms of test case feasibility due to call dependences and runtime exceptions. In this paper, we present a search-based approach to automatically generating test cases for object-oriented software. It relies on a tree-based representation of method call sequences. Strongly-typed genetic programming is employed to generate method call trees which respect the call dependences among the methods. We apply a new kind of distance-based fitness function that accounts for runtime exceptions. In a case study, the approach outperformed random testing in terms of achieved coverage and it produced test cases achieving full branch coverage for a test object that makes ample use of explicit runtime exceptions.