Mutation testing is a fault-oriented software testing technique, and a test suite generated based on the criterion of mutation testing generally has a high capability in detecting faults. A mutant that is hard killed is called a stubborn one. The traditional methods of test data generation often fail to generate test data that kill stubborn mutants. To improve the efficiency of killing stubborn mutants, in this article, we propose a method of generating test data by dynamically reducing the search domain under the criterion of strong mutation testing. To fulfill this task, we first present a method of measuring the stubbornness of a mutant based on the reachability condition of a mutated statement. Then, we formulate the problem of generating test data to kill the mutant as an optimization one with a unique constraint. Finally, we generate test data using a coevolutionary genetic algorithm. Given the fact that the domain of test data that kills a stubborn mutant is generally small, we adopt a method of dynamically reducing the search domain to improve the efficiency of the algorithm. We apply the proposed method to test eight benchmark and industrial programs. The experimental results demonstrate that the proposed method has capabilities in seeking stubborn mutants and efficiently generating test data to kill stubborn mutants.

中文翻译：

---

通过动态减少搜索域有效生成测试数据以杀死顽固的突变体

变异测试是一种面向故障的软件测试技术，基于变异测试准则生成的测试套件通常具有较高的故障检测能力。难以杀死的突变体被称为顽固的突变体。传统的测试数据生成方法往往无法生成杀死顽固突变体的测试数据。为了提高杀死顽固突变体的效率，在本文中，我们提出了一种在强突变测试标准下通过动态缩小搜索域来生成测试数据的方法。为了完成这个任务，我们首先提出了一种基于突变语句的可达性条件来测量突变体顽固性的方法。然后，我们将生成测试数据以杀死突变体的问题制定为具有唯一约束的优化问题。最后，我们使用协同进化遗传算法生成测试数据。鉴于杀死顽固突变体的测试数据域一般较小，我们采用动态缩小搜索域的方法来提高算法的效率。我们应用所提出的方法来测试八个基准和工业程序。实验结果表明，所提出的方法具有寻找顽固突变体和有效生成测试数据以杀死顽固突变体的能力。