Integrating Evolutionary Testing with Reinforcement Learning for Automated Test Generation of Object-Oriented Software

Recent advances in evolutionary test generation greatly facilitate the testing of Object-oriented (OO) software. Existing test generation approaches are still limited when the Software under test (SUT) includes Inherited class hierarchies (ICH) and Non-public methods (NPM). This paper presents an approach to generate test cases for OO software via integrating evolutionary testing with reinforcement learning. For OO software with ICH and NPM, two kinds of particular isomorphous substitution actions are presented and a Q-value matrix is maintained to assist the evolutionary test generation. A prototype called EvoQ is developed based on this approach and is applied to generate test cases for actual Java programs. Empirical results show that EvoQ can efficiently generate test cases for SUT with ICH and NPM and achieves higher branch coverage than two state-of-the-art test generation approaches within the same time budget.