Class/aspect level mutation operators in aspect-oriented software systems

Bhatia, Vasundhara; Singhal, Abhishek

doi:10.1109/iicip.2016.7975397

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Test data generation，基于约束的生成方法) [9]、DDR (Dynamic Domain Reduction test data generation，动态域削减方法) [10]、DRD (Domain Reduction approach with Data dependence，考虑数据依赖的域削减方法) [7] 和 GA (Generation Algorithm，遗传算法生成变异测试用例的方法) [ Table 1. Branch distance calculation table …”

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A Method Combined with Data Flow Constraint for Generating Mutation Test Cases

杜¹

2018

SEA

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Mutation testing is a powerful method of software testing. As a means of finding faults and uncovering test suite defects, the method of test case generation is crucially important. If the test case can generate efficiently and achieve a higher mutation score, it has the potential to help mutation testing be widely adopted. At present, most of the mutation test case generation methods are based on the analysis of the control flow graph, using only the control flow constraint between the statements to guide the generation of test cases, without considering the influence of the data flow constraint among the statements. In this paper, a mutation test case generation method combined with data flow constraint is proposed. First, the control flow constraint and the data flow constraint were combined to model the fitness function; second, the model was used in genetic algorithms to guide the selection and evolution of the test cases. Experiments show that the average iteration number is reduced by 60.53% when generating the same scale test set compared to HGA, and the generated test cases get a 27.9% higher mutation score than random method, showing a better error detection ability.

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Section: 引言unclassified