An Improved Automated Test Data Generation Method for Object-Oriented C++ Projects

C++ is a powerful language that provides numerous flexible and efficient features, however, it also presents significant challenges in detecting and correcting errors, especially in objectoriented projects. While a test case for a procedure program includes a sequence of input values to be provided during execution along with the expected outputs, test cases for classes under test must also consider the state of the object on which the method is invoked. To overcome this difficulty, Genetic Algorithm are utilized to generate test cases for class unit testing automatically. However, due to its reliance on randomness, Genetic Algorithm faces difficulties in solving complex conditions in conditional blocks, resulting in lower code coverage. Meanwhile, solving complex conditions in conditional blocks is precisely the strength of Concolic Execution. This paper proposes a method, named GenColic, that integrates Concolic Execution into Genetic Algorithm to generate automated test data for object-oriented C++ projects. The core focus of this method is to insert a Concolic Execution Phase after the selection process of the Evolutionary loop in Genetic Algorithm. By using Concolic Execution, a new test case that covers an execution path not yet covered by the test suite is added. This results in offspring with better coverage, which continues to undergo Crossover and Mutation. Experiments and comparisons were conducted on Concolic Execution, GenColic, and Genetic Algorithm applied to object-oriented C++ open-source projects. The results show that GenColic can generate test suites with higher branch and statement coverage.