Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

ABSTRACT Accurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, da...

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Published in:Software testing, verification & reliability Vol. 34; no. 8
Main Authors: Yang, Minghao, Yang, Shunkun, Bian, Chong
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 01.12.2024
Subjects:
Autocorrelation
Coders
Datasets
encoder‐decoder gated recurrent unit networks
ensemble empirical mode decomposition
Evolutionary algorithms
Machine learning
prediction lag
Quality assessment
Quality assurance
Software development
Software quality
Software reliability
software reliability growth models
software reliability prediction
ISSN:0960-0833, 1099-1689
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Summary:ABSTRACT Accurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance. Prediction lags caused by the autocorrelation and nonstationarity of software fault data affect the software reliability prediction performance. Our proposed EEMD‐ED‐GRU‐PF model can effectively reduce the prediction lags and achieve best prediction performance in eight public datasets compared with four nonparametric and five parametric baseline methods.
Bibliography:This work was supported in part by the National Natural Science Foundation of China (grant number 61672080).
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ISSN:0960-0833
1099-1689
DOI:10.1002/stvr.1895