A recent survey using industrial projects has shown that providing an estimate of the lifetime of pull requests to developers helps to speed up their conclusion. Previous work has explored pull request lifetime predic...
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A recent survey using industrial projects has shown that providing an estimate of the lifetime of pull requests to developers helps to speed up their conclusion. Previous work has explored pull request lifetime prediction in open-source projects using regression techniques but with a broad margin of error. The first objective of our work was to reduce the average error rate of the prediction obtained by the regression techniques so far. We performed experiments with different regression techniques and achieved a significant decrease in the mean error rate. The second objective of our work was to obtain a more effective and useful predictive model that can classify pull requests according to five discrete time intervals. We proposed new predictive attributes for the estimation of the time intervals and employed attribute selection strategies to identify subsets of attributes that could improve the predictive behavior of the classifiers. Our classification approach achieved the best accuracy in all the 20 projects evaluated in comparison with the literature. The average accuracy was of 45.28% to predict pull request lifetime, with an average normalized improvement of 14.68% in relation to the majority class and 6.49% in relation to the state-of-the-art.
Dependability analysis is the main method to evaluate the design of safety-critical systems, which is able to analyze the source of the faults and find them as early as possible. With the increasing system scale and c...
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Dependability analysis is the main method to evaluate the design of safety-critical systems, which is able to analyze the source of the faults and find them as early as possible. With the increasing system scale and complexity, Model-based Dependability Analysis (MDBA) has become the mainstream, so that it is crucial to provide powerful models that accurately reflect the real systems and easy to be built. However, modeling a system is error-prone and it is difficult to verify the correctness of the model being built. Therefore, this paper proposes our dependability modeling tool called DATAM (Dependability Analysis Tool of AltaRica Model), which is based on AltaRica, a dataFlow language. We present a method for converting key elements of AltaRica into model/GUI components, thereby ensuring the consistency of the model with the modeling language. GUI-based operations and rich custom components ensure ease of using DATAM. Besides, some components of the model can be edited directly through the AltaRica script and can be interchanged with GUI components. Finally, DATAM supplies varieties of reliability calculation functions. We demonstrate the ability of DATAM through a case study and compare the results with SimFia education version.
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