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In this study, the geometric process (GP) model is considered in order to calculate the debugging and testing costs of a software product. Under the assumption of the GP model, the debugging and testing costs of the software product are obtained depending on the first and second moment functions of the GP. It is observed that the values of the first and second moment functions of the process must be known in order to calculate the debugging and testing costs. At the same time, the calculation of moment functions also depends on both the distribution of the first interarrival time of the GP and the estimates of the model and distribution parameters. In this study, the proposed debugging and testing costs are calculated for the data set containing 136 failure times of a real-time command and control system. For this dataset, it has been shown in previous studies that the GP with gamma distribution can be proposed as a model. Under gamma distribution assumption, the maximum likelihood estimates of the model parameters are obtained. Using the estimates of the model parameters, the first and second moment functions of the GP are calculated with the help of the numerical methods proposed for these functions. Finally, the debugging and testing costs are obtained for the data set. [ABSTRACT FROM AUTHOR]

Since December 2019, the Covid-19 virus afftected our lives and continues to affect the whole world significantly. The investigistion of the indicators of the Covid-19 virus and vaccination studies are of great interest to overcome the Covid- 19 pandemic based on the World health organization recommendations. In this context, many scientific studies have revealed valuable information for the future of the virus. In this study, estimation of the cOvid-19 cases and Classification of changes in the daily number of cases in Turkey was carried out by using support vector machine and k-nearest neighbor algorithms. The indicators that play a critical role in the estimation of the daily patient number classification have been determined as "positivity rate", "fillation rate", "workplace mobility" and "mobility in parks". It has been observed that the k-nearest neighbor algorithm (84.7%) is the most successful algorithm in the estimation of the daily number of cases when considering the highlighted features. [ABSTRACT FROM AUTHOR]