Ontology based negative selection approach for mutation testing

Mutation testing is used to design new software tests and evaluate the quality of existing software tests. It works by seeding faults in the software program, which are called mutants. Test cases are executed on these mutants to determine if they are killed or remain alive. They remain alive because some of the mutants are syntactically different from the original, but are semantically the same. This makes it difficult for them to be identified by the test suites. Such mutants are called equivalent mutants. Many approaches have been developed by researchers to discover equivalent mutant but the results are not satisfactory. This research developed an ontology based negative selection algorithm (NSA), designed for anomalies detection and similar pattern recognition with two-class classification problem domains, either self (normal) or non-self (anomaly). In this research, an ontology was used to remove redundancies in test suites before undergoing detection process. During the process, NSA was used to detect the equivalent mutant among the test suites. Those who passed the condition set would be added to the equivalent coverage. The results were compared with previous works, and showed that the implementation of NSA in equivalent mutation testing had minimized local optimization problem in detector convergence (number of detectors) and time complexity (execution time). The findings had more equivalent mutants with average of 91.84% and scored higher mutation score (MS) with average of 80% for all the tested programs. Furthermore, the NSA had used a minimum number of detectors for higher detection of equivalent mutants with the average of 78% for all the tested programs. These results proved that the ontology based negative selection algorithm had achieved its goals to minimize local optimization problem

Negative selection approach for equivalent mutant detection

Mutation testing is not a new thing in software engineering studies nowadays. The researcher used this technique to assess the quality of test suite. Many researches already done and proved that this technique is more efficient compare to other approaches. In mutation testing, the important question is mostly about equivalent mutant. This is because the detection of equivalent mutant affects the result for mutation score. It is a must to compute a correct mutation score. So, to solve the problem, this paper introduced a method called negative selection. In particularly this approach is based on the self and non-self rules that function to detect abnormal behaviors of the program that contrast to the normal functioning of the system. We also present the algorithm and first empirical result

Ontology of mutation testing for java operators

Operators are special characters within the Java language to manipulate primitive data type. Java operators can be classified as unary, binary and ternary. The design of Java operator sometimes becomes confusing when it come s to testing tool s as they had the same function with different label in every testing tool . Therefore, in order to map the knowledge of operators correctly, this research has proposed ontology that is dedicated to mutation testing as a means to define the formal specification of concepts and documentation of knowledge of Java operators. Existing papers on ontology did not specify further on entities and proper ties of operators. Some papers only focus on mutation testing but not the operators. Thus, this paper will present the ontology clearly with the aim to ease end user to identify and understand every classes, properties and relations in Java operators