基于遗传-BP算法的煤矿安全监控系统测试研究

Test case study of coal mine safety monitoring system based on genetic-BP algorithm

针对煤矿监控系统的测试要求,提出了一种自动生成测试用例数据的方法用于煤矿监控系统的测试任务。该方法利用遗传算法和BP算法对要测试的功能模块和逻辑结构进行分析解读,通过全局寻优得出最佳测试路径和测试数据,对于提高测试的精准性和有效性有很大优势,而且相对与人工测试更加安全。新的测试用例生成算法结合遗传算法和BP算法,能充分发挥遗传算法的全局寻优能力和BP算法学习能力强的优势,使得数据生成更快捷可靠。选取煤矿监控系统的部分功能测试作为实验材料,与人工测试方法和利用遗传该算法生成测试数据进行了比较。结果表明,该新方法能够更好的检测出煤矿监控系统中的错误,发现错误的数量和时间都较人工测试要好,对于提高煤矿安全监控系统的准确性和稳定性有较好的帮助。

For the testing requirements of Coal Mine Monitoring System,an automatic method of generating test case data monitoring system was put forward for coal testing task.The method adopted genetic algorithm and BP algorithm to test the function modules and the logical structure of interpretation,global optimization by optimum test path and test data,for improving the accuracy and validity of the test with great advantages,and relatively more secure and manual testing.New test case generation algorithm combining genetic algorithm and BP algorithm,genetic algorithm could give full play to the global optimization algorithm for learning ability and strong BP advantage,making more efficient and reliable data generation.The part of Coal Mine Monitoring System function test were selected as the experimental materials,and artificial methods of testing and use of genetic algorithm to generate test data were compared.The results showed that the new method are better able to detect errors in the Coal Mine Monitoring System and the quantity and time of error detection is better than manual testing.It helps to improve coal mine safety monitoring system for the accuracy and stability.