Software testing refers to a process which improves the quality of software systems and also is one of time and cost consuming stages in software development. Hence, software test automation is regarded as a solution which can facilitate heavy and laborious tasks of testing. Automatic generation of test data with maximum coverage of program branches is regarded as an NP-complete optimization problem. Several heuristic and evolutionary algorithms have been proposed for generating test suits with maximum coverage. Failure to maximally branch coverage, poor success rate in test data generation with maximum coverage and lack of stable results are considered as the major drawbacks of previous methods. Enhancing the coverage rate of the generated test data, enhancing the success rate in generating the tests data with maximum coverage and enhancing the stability and speed criteria are the major purposes of the present study. In this study, an effective method (Traxtor) is proposed to automatically generate tests data by using imperialist competitive algorithms (ICA) optimization algorithms. The proposed method is aimed at generating test data with maximum branch coverage in a limited amount of time. The results obtained from executing a wide range of experiments indicated that the proposed algorithm, with 99.99% average coverage, 99.94% success rate, 2.77 average generation and 0.12 s average time outperformed the other algorithms.

软件测试是指提高软件系统质量的过程，也是软件开发中耗费时间和成本的阶段之一。因此，软件测试自动化被认为是一种可以促进繁重而费力的测试任务的解决方案。自动生成具有最大程序分支覆盖率的测试数据被认为是一个NP完全优化问题。已经提出了几种启发式和进化算法来生成具有最大覆盖率的测试套装。未能最大化分支覆盖，最大覆盖测试数据生成的成功率低以及缺乏稳定的结果被认为是先前方法的主要缺点。提高生成的测试数据的覆盖率，提高生成具有最大覆盖率的测试数据的成功率以及提高稳定性和速度标准是本研究的主要目的。在这项研究中，提出了一种有效的方法（Traxtor），通过使用帝国主义竞争算法（ICA）优化算法自动生成测试数据。所提出的方法旨在在有限的时间内生成具有最大分支覆盖率的测试数据。大量实验的结果表明，该算法在平均覆盖率 99.99%、成功率 99.94%、平均生成时间 2.77 和平均时间 0.12 s 方面优于其他算法。提出了一种有效的方法（Traxtor），利用帝国主义竞争算法（ICA）优化算法自动生成测试数据。所提出的方法旨在在有限的时间内生成具有最大分支覆盖率的测试数据。大量实验的结果表明，该算法在平均覆盖率 99.99%、成功率 99.94%、平均生成时间 2.77 和平均时间 0.12 s 方面优于其他算法。提出了一种有效的方法（Traxtor），利用帝国主义竞争算法（ICA）优化算法自动生成测试数据。所提出的方法旨在在有限的时间内生成具有最大分支覆盖率的测试数据。大量实验的结果表明，该算法在平均覆盖率 99.99%、成功率 99.94%、平均生成时间 2.77 和平均时间 0.12 s 方面优于其他算法。