Chen Li
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, 820--8502, Japan.

Junjun Zheng
Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, 525--8577, Japan.

Hiroyuki Okamura
Graduate School of Advanced Science Engineering, Hiroshima University, Higashihiroshima, 739--8527 Japan.

Tadashi Dohi
Graduate School of Advanced Science Engineering, Hiroshima University, Higashihiroshima, 739--8527 Japan.

DOI https://doi.org/10.33889/IJMEMS.2022.7.3.022

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Abstract

With the growing demand for high reliability and safety software, software reliability prediction has attracted more and more attention to identifying potential faults in software. Software reliability growth models (SRGMs) are the most commonly used prediction models in practical software reliability engineering. However, their unrealistic assumptions and environment-dependent applicability restrict their development. Recurrent neural networks (RNNs), such as the long short-term memory (LSTM), provide an end-to-end learning method, have shown a remarkable ability in time-series forecasting and can be used to solve the above problem for software reliability prediction. In this paper, we present an attention-based encoder-decoder RNN called EDRNN to predict the number of failures in the software. More specifically, the encoder-decoder RNN estimates the cumulative faults with the fault detection time as input. The attention mechanism improves the prediction accuracy in the encoder-decoder architecture. Experimental results demonstrate that our proposed model outperforms other traditional SRGMs and neural network-based models in terms of accuracy.

Keywords- Software reliability, Recurrent neural networks (RNNs), Long short-term memory (LSTM), Encoder-decoder, Attention mechanism

Citation

Li, C., Zheng, J., Okamura, H., & Dohi, T. (2022). Software Reliability Prediction through Encoder-Decoder Recurrent Neural Networks. International Journal of Mathematical, Engineering and Management Sciences, 7(3), 325-340. https://doi.org/10.33889/IJMEMS.2022.7.3.022.