Software reliability prediction using a deep learning model based on the RNN encoder–decoder

Jinyong Wang and Ce Zhang

Reliability Engineering and System Safety, 2018, vol. 170, issue C, 73-82

Abstract: 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.

Keywords: Deep learning model based on RNN encoder–decoder; Model comparison; Neural network models; Parameter models; Software reliability (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:170:y:2018:i:c:p:73-82

DOI: 10.1016/j.ress.2017.10.019

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