استراتژی الهام گرفته از فیزیک درحل مسائل دشوار
  •   آرشیو : نسخه پاییز 1398
  •   نوع مقاله : پژوهشی
  •   کد پذیرش : 1348
  •   موضوع : سایر شاخه های علوم رایانه
  • نویسنده/گان : امیراحمد نیری
  •   کلید واژه : نقص نرم افزار، پيش بيني نقص، درخت نحو انتزاعی، شبکه عصبی عمیق، شبکه باور عمیق
  •   Abstract :
    The most important research field in forecasting methods in software discussion is software prediction prediction and so far significant studies and researches have been done in this field. In corporate organizations, minor software defects may lead to the loss of the manufacturing industry and ultimately lead to reduced customer satisfaction. Most current research has improved forecasting operations due to the deployment of optimization techniques, but there is no possibility of more precise granulation such as change level, module level and packaging level. In other words, little attention has been paid to examining and predicting that new (albeit minor) software changes may contain defects. For this reason, in this study, an abstract syntax-based method combined with a deep belief network algorithm for predicting conscious defect is presented. The key idea of the proposed approach is to use abstract trees, DBNs and different classifiers. The combination of abstract syntax trees and deep belief networks for classification as well as software review is considered as a new hybrid approach. In fact, in order to extract, optimize the features and classify the software from the abstract syntax tree and to learn in the processes, a deep belief network is used. The MC1 dataset will be used to evaluate the proposed method. The evaluation results of the proposed method show a growth of 0.6% and 16% in terms of accuracy criteria compared to the basic article method, respectively.
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