Multiple-Searching Genetic Algorithm for Whole Test Suites

A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

Cooperative verifier-based testing with CoVeriTest

Testing is a widely applied technique to evaluate software quality, and coverage criteria are often used to assess the adequacy of a generated test suite. However, manually constructing an adequate test suite is typically too expensive, and numerous techniques for automatic test-suite generation were proposed. All of them come with different strengths. To build stronger test-generation tools, different techniques should be combined. In this paper, we study cooperative combinations of verification approaches for test generation, which exchange high-level information. We present CoVeriTest, a hybrid technique for test-suite generation. CoVeriTest iteratively applies different conditional model checkers and allows users to adjust the level of cooperation and to configure individual time limits for each conditional model checker. In our experiments, we systematically study different CoVeriTest cooperation setups, which either use combinations of explicit-state model checking and predicate abstraction, or bounded model checking and symbolic execution. A comparison with state-of-the-art test-generation tools reveals that CoVeriTest achieves higher coverage for many programs (about 15%).