A novel approach to generating test cases with genetic programming

Abstract

Part of the automating software testing procedure includes the automation of test cases. Automation can lower the cost and effort and at the same time can increase the quality of test cases and consequently the testing procedure. Many different approaches for test case generation are available: generation from code, formal methods and different models, among others also from UML diagrams, more precisely from UML activity diagrams. Researchers use different techniques, of which genetic programming (GP) is very popular and was used in our research. In the proposed approach we generated test cases from the UML activity diagram, from which we constructed the binary decision tree structure, which is used as an instance in the evolution process of GP. The default tree structure is used throughout the whole evolution process, only the content (the testing parameters) of the nodes changes. The process of evolution consists of several genetic operators, such as selection, crossover and mutation. The main novelty of our method is a different fitness function than we can find in existing literature. In contrast to related work where the coverage is used - we used the error occurrence for our metric. The proposed method is demonstrated on the example of an automated teller machine (ATM), where we show how the full automation of test case generation and testing is a major advantage of our method.