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首页> 外文期刊>International journal of software engineering and knowledge engineering >An Efficient Method to Generate Test Data for Software Structural Testing Using Artificial Bee Colony Optimization Algorithm
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An Efficient Method to Generate Test Data for Software Structural Testing Using Artificial Bee Colony Optimization Algorithm

机译：人工蜂群优化算法在软件结构测试中生成测试数据的有效方法

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artificial bee colony algorithm; automated test data generation; branch coverage; Software testing%Software testing is a process for determining the quality of software system. Many small and medium-sized software projects can be manually tested. Nevertheless, due to the widespread extension of software in large-scale projects, testing them will be highly time consuming and costly. Hence, automated software testing (AST) is considered to be as a solution which can ease and simplify heavy and cumbersome tasks involved in software testing. For AST, certain data are needed through which the quality of systems can be evaluated. In this paper, an artificial bee colony (ABC) algorithm was used for solving the issue of test data generation and branch coverage criterion was used as a fitness function for optimizing the proposed solutions. For doing comparisons, seven well-known and traditional programs in the literature were used as benchmarks. The experimental results indicate that our method, on average, outperforms simulated annealing, genetic algorithm, particle swarm optimization and ant colony optimization based on the following four criteria: 99.99% average branch coverage, 99.94% success rate, 3.59 average convergence generation and 0.18ms average execution time.
机译：人工蜂群算法；自动测试数据生成；分支机构覆盖；软件测试％软件测试是确定软件系统质量的过程。可以手动测试许多中小型软件项目。然而，由于软件在大型项目中的广泛扩展，对其进行测试将非常耗时且成本高昂。因此，自动化软件测试（AST）被认为是可以减轻和简化软件测试中繁重而繁琐任务的解决方案。对于AST，需要某些数据，通过这些数据可以评估系统的质量。本文采用人工蜂群算法（ABC）解决测试数据生成问题，并以分支覆盖度准则作为适应度函数来优化所提出的解决方案。为了进行比较，使用了文献中的七个著名和传统程序作为基准。实验结果表明，基于以下四个标准，我们的方法平均表现优于模拟退火，遗传算法，粒子群优化和蚁群优化：平均分支覆盖率99.99％，成功率99.94％，平均会聚生成3.59和0.18ms平均执行时间。

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