کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7195304 1468200 2018 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Software reliability prediction using a deep learning model based on the RNN encoder-decoder
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی مکانیک
پیش نمایش صفحه اول مقاله
Software reliability prediction using a deep learning model based on the RNN encoder-decoder
چکیده انگلیسی
Different software reliability models, such as parameter and non-parameter models, have been developed in the past four decades to assess software reliability in the software testing process. Although these models can effectively assess software reliability in certain testing scenarios, no single model can accurately predict the fault number in software in all testing conditions. In particular, modern software is developed with more sizes and functions, and assessing software reliability is a remarkably difficult task. The recently developed deep learning model, called deep neural network (NN) model, has suitable prediction performance. This deep learning model not only deepens the layer levels but can also adapt to capture the training characteristics. A comprehensive, in-depth study and feature excavation ultimately shows the model can have suitable prediction performance. This study utilizes a deep learning model based on the recurrent NN (RNN) encoder-decoder to predict the number of faults in software and assess software reliability. Experimental results show that the proposed model has better prediction performance compared with other parameter and NN models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Reliability Engineering & System Safety - Volume 170, February 2018, Pages 73-82
نویسندگان
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