Most of metamorphic testing (MT) research works focused on the generation and application of metamorphic relations (MRs). There is no clear conclusion about the relationship between test case generation methods and performance of MT. In this article, we introduce a novel method based on adaptive random testing (ART) and MR for MT test case generation. It proposes a family of algorithms for MT test cases generation, named as MT based ART (MT-ART). Three distances are measured to generate the next MT test case. In order to verify the performance of this method, series of experiments on four programs with different numbers of inputs are introduced. The results show that MT-ART performs better than other ART algorithms not only in test effectiveness, but also in test efficiency and test coverage. Based on this article, the following conclusions can be drawn: first, considering the effectiveness of MRs and test cases in MT may lead to better results. In this way, most of the existing research can be improved by this method. This is the most important contribute of our research. Second, not only the source test cases, but also the follow-up test cases can improve the performance of MT. Therefore, they should be considered together during the process of the next test case generation. Third, the average distance performs better than the max distance and the minimum distance in metamorphic test case selection.

Test suite reduction is an effective way to reduce the cost of regression testing by identifying and removing redundant test cases from the original test suite. In this paper, we propose a novel cluster test suite reduction using an evolutionary multi-objective optimization algorithm. Specifically, we use a K-means algorithm to group similar test cases to the same cluster. Then the evolutionary algorithm is used to remove redundant test cases based on the clustering results, and optimization objects are represented as the coverage-related criteria, fault-related criteria and cost-related criteria. The experimental results involving eight subject programs show that the proposed method can outperform the other three state-of-the-arts with respect to both fault detection (4.61% -9.44%) and reduction ratio (4.10% -10.64%). Meanwhile, the experiments also prove that our method has a better performance of missing failure rate (0.049% -0.132%) and code coverage rate (3.34% -6.10%). Besides, the proposed method costs are found to be comparable to the other techniques.

A geographic information system (GIS) provides basic location-enabled services for many different applications related to navigation, education, and telecommunications. It is a foundation for analysis and visualization. Testing GIS is critical, but challenging due to the difficulty to assess the correctness of GIS outputs, which is called the test oracle problem of software testing. Metamorphic testing alleviates the problem by constructing metamorphic relations (MRs) among multiple inputs and outputs of the program under test. In this article, a semiautomated metamorphic testing (SAMT) method, based on the formal MR model and an improved adaptive random testing algorithm, was proposed to the GIS. To evaluate the performance of our approach, we conducted a case study on a superficial area calculation program, a typical component of GIS. Six kinds of MR construction methods were suggested for the GIS domain program testing. The experimental results show that SAMT can detect the mutations effectively that could solve the test oracle problem efficiently. More importantly, there is no need to manual participation in the testing process, except for the MR construction.

Metamorphic testing has been achieved great application breakthrough in many different domains in the last decades and great deals of valuable metamorphic relations (Mrs) were generated for different software and programs. When one tries to reuse these Mrs in different domain at different abstraction levels, textual descriptions are not informative and expressive enough. Based on these observations, we propose a formal model for MR through improving the definition of testing success. With the formal model, we construct a decomposition model for MR, and provide three sub-relations for decrease complexity of MR.

A software security testing behavior model, SETAM, was proposed in our previous work as the integrated model for describing software security testing requirements behavior, which is not only compatible with security functions and latent typical misuse behaviors, but also with the interaction of them. In this paper, we analyze the differences between SETAM with security use case and security misuse case in different types of security test requirements. To illustrate the effectiveness of SETAM, we compare them in a practical case study by the number of test cases and the number of faults detected by them. The results show that SETAM could decrease about 34.87% use cases on average, and the number of faults detected by SETAM increased by 71.67% in average, which means that our model can detect more faults with fewer test cases for software security testing.

Integer overflow is one of the most dangerous defects for programs. Many kinds of static analysis techniques and dynamic test methods have been provided to detect it, not only for programs with source code but also with binary code. One of the most i