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Developing and testing algorithms for stopping testing, screening, run-in of large systems or programs

机译:开发和测试算法以停止大型系统或程序的测试,筛选,运行

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When large hardware-software systems are run-in or an acceptance testing is made, a problem is when to stop the test and deliver/accept the system. The same problem exists when a large software program is tested with simulated operations data. Based on two theses from the Technical University of Denmark, the paper describes and evaluates 7 possible algorithms. Of these algorithms, the three most promising are tested with simulated data. 27 different systems are simulated, and 50 Monte Carlo simulations made on each system. The stop times generated by the algorithm is compared with the known perfect stop time. Of the three algorithms two is selected as good. These two algorithms are then tested on 10 sets of real data. The algorithms are tested with three different levels of confidence. The number of correct and wrong stop decisions are counted. The conclusion is that the Weibull algorithm with 90% confidence level takes the right decision in every one of the 10 cases.
机译:当运行大型硬件软件系统或进行验收测试时,问题在于何时停止测试并交付/接受系统。当使用模拟的操作数据测试大型软件程序时,存在相同的问题。基于丹麦技术大学的两篇论文,本文描述并评估了7种可能的算法。在这些算法中,最有前途的三个算法已通过模拟数据进行了测试。模拟了27个不同的系统,并且在每个系统上进行了50个蒙特卡洛模拟。将算法生成的停止时间与已知的理想停止时间进行比较。在这三种算法中,最好选择两种。然后在10组真实数据上测试这两种算法。以三种不同的置信度对算法进行了测试。计算正确和错误停止决策的数量。结论是,置信水平为90%的Weibull算法在10个案例中的每一个案例中都做出了正确的决策。

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