A Method of Automatic Test Case Generation Based on CT-LSSVM Algorithm in FAO Systems

Sha Wang, Qingyuan Shang, Zhujun Ling, Dandan Liu, Xiangxian Chen
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Abstract

The FAO system is a new-generation railway signaling system, the comprehensive and accurate testing is the main means to verify the safety and stability of the system, and the design and generation of test cases is an important link of the system testing. Traditionally, test cases are manually generated, which is inefficient, time-consuming and inaccurate. To solve this problem, we proposed an automatic test case generation method for the FAO system specified scenario using the CT-LSSVM algorithm. The CT algorithm was used to realize multi-factor combination to generate test cases, and the LSSVM method was used to predict and analyze the expected results of the test cases. The results showed that when the LSSVM method was used to model and analyze the test cases generated by the CT five-factor combination, the recognition rate of the calibration set was 96.58% and the recognition rate of the test set was 97.73%; At the same time, some test cases of the twelve-factor combination were predicted and analyzed, and the recognition rate reached 98.57%. This proves that the CT-LSSVM method can be applied to the test case generation of the FAO system.
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FAO系统中基于CT-LSSVM算法的测试用例自动生成方法
FAO系统是新一代铁路信号系统,全面准确的测试是验证系统安全性和稳定性的主要手段,测试用例的设计和生成是系统测试的重要环节。传统上,测试用例是手工生成的,这是低效、耗时和不准确的。为了解决这一问题,我们提出了一种基于CT-LSSVM算法的FAO系统指定场景的自动测试用例生成方法。采用CT算法实现多因素组合生成测试用例,采用LSSVM方法对测试用例的预期结果进行预测和分析。结果表明:当使用LSSVM方法对CT五因子组合生成的测试用例进行建模和分析时,校准集的识别率为96.58%,测试集的识别率为97.73%;同时,对12因素组合的部分测试用例进行预测分析,识别率达到98.57%。这证明了CT-LSSVM方法可以应用于FAO系统的测试用例生成。
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