LSTM Encoder-Decoder Dropout Model in Software Reliability Prediction.

Document Type : Original Research Article

Authors

Department of Algorithms and Computation, School of Engineering Sciences, College of Engineering, University of Tehran, Tehran, IRAN

Abstract

Numerous methods have been introduced to predict the reliability of software. In general, these methods can be divided into two main categories, namely parametric (e.g. software reliability growth models) and non-parametric (e.g. neural networks). Both approaches have been successfully implemented in software testing applications over the past four decades. Since most software reliability prediction data are available in the form of time series, deep recurrent network models (e.g. RNN, LSTM, NARX, and LSTM Encoder-Decoder networks) are considered as powerful tools to be employed in reliability-related problems. However, the problem of overfitting is a major concern when using deep neural networks for software reliability applications. To address this issue, we propose the use of dropout; therefore, this study utilizes a deep learning model based on LSTM Encoder-Decoder Dropout 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 RNN-based models.

Keywords

Main Subjects

References

  1. Soltanali, Rohani, A., Abbaspour-Fard, M. H., &J. T. Farinha, "A comparative study of statistical and soft computing techniques for reliability prediction of automotive manufacturing", Applied Soft Computing, vol. 98, 106738.‏ 2021.
  2. Oveisi, , & R. Ravanmehr, "SFTA-Based Approach for Safety/Reliability Analysis of Operational Use-Cases in Cyber-Physical Systems", Journal of Computing and Information Science in Engineering, vol. 17 no.3, 2017.
  3. Oveisi, M. A. Farsi, M. Nadjafi, & A. Moeini, "A New Approach to Promote Safety in the Software Life Cycle," Journal of Computer & Robotics, vol. 12, no.1, pp.77-91,‏2019.
  4. Oveisi, M. A. Farsi, M. Nadjafi, A. Moeini, & M. habankhah," Design Software Failure Mode and Effect Analysis using Fuzzy TOPSIS Based on Fuzzy Entropy," Journal of Advances in Computer Engineering and Technology, vol.6, no. 3, pp.171-180,2020.
  5. D. Van Driel, J. W. Bikker, & M. Tijink, "Prediction of software reliability," Microelectronics Reliability, vol. 119, 114074,‏2021.
  6. Oveisi, &M. A. Farsi, "Software safety analysis with UML-Based SRBD and fuzzy VIKOR-Based FMEA, "International Journal of Reliability, Risk and Safety: Theory and Application, vol.1,no.1,pp.35-44, 2018.
  7. Aggarwal, V. K. Gupta, "Software reliability growth model," International Journal of Advanced Research in Computer Science and Software Engineering, vol.4, no.1, 2014.
  8. Jaiswal, & R. Malhotra, "Software reliability prediction using machine learning techniques," International Journal of System Assurance Engineering and Management, vol.9,no.1, pp.230-244,2018.
  9. Sahu, & R. K. Srivastava, "Revisiting software reliability, "in Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, Balas, V., Sharma, N., Chakrabarti, A. (eds), vol. 808. Springer, Singapore.
  10. Wang, & C. Zhang, "Software reliability prediction using a deep learning model based on the RNN encoder–decoder, "Reliability Engineering & System Safety, vol.170, pp.73-82, 2018.
  11. Borovykh, S. Bohte, & C. W. Oosterlee, "Conditional time series prediction with convolutional neural networks, "Journal of Computational Finance, vol.22, no.4.2018.
  12. Che, S. Purushotham, K. Cho, D. Sontag, &Y. Liu, "Recurrent neural networks for multivariate time series with missing values," Scientific reports, vol.8,no.1, pp.1-12, 2018.
  13. Wang, H., Yang, Z., Yu, Q., Hong, T., & Lin, X. (2018). Online reliability time series prediction via convolutional neural network and long short term memory for service-oriented systems. Knowledge-Based Systems, 159, 132-147.‏
  14. L. H. Nguyen, "Uncertainty in Recurrent Neural Network with Dropout", Master Thesis. Department of Computer, Communication and Information Sciences, AALTO University, 2020.
  15. Gal, & Z. Ghahramani, "A theoretically grounded application of dropout in recurrent neural networks," Advances in neural information processing systems, vol.29, pp.1019-1027, 2016.
  16. Labach, H. Salehinejad, &, S. Valaee "Survey of dropout methods for deep neural networks" arXiv preprint arXiv:1904.13310.2019.
  17. Di Nunno, & F.Granata, "Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network" Environmental Research, vol.190, 110062,‏2020.
  18. Gal & Z. Ghahramani, "Dropout as a bayesian approximation: Representing model uncertainty in deep learning," In International conference on machine learning, 2016, pp. 1050-1059, PMLR..‏
  19. Jeong, S. Kim, & K. Yi, "Surround vehicle motion prediction using LSTM-RNN for motion planning of autonomous vehicles at multi-lane turn intersections, "IEEE Open Journal of Intelligent Transportation Systems, vol.1, pp.2-14,‏ 2020.
  20. Lv, , Xu, J., Zheng, K., Yin, H., Zhao, P., & Zhou, X. "Lc-rnn: A deep learning model for traffic speed prediction" . IJCAI,pp. 3470-3476,2018.‏
  21. Srivastava, "Improving neural networks with dropout"., MSc thesis, University of Toronto, 2013.
  22. Sutskever, O. Vinyals, &Q. V. Le, "Sequence to sequence learning with neural networks, "Advances in neural information processing systems, pp. 3104-3112, 2014.
  23. Pascanu, C. Gulcehre, K. Cho, &Y. Bengio, "How to construct deep recurrent neural networks". arXiv preprint arXiv:1312.6026.‏2013.
  24. Wang, Z. Yang, Q. Yu, T. Hong, &X. Lin, "Online reliability time series prediction via convolutional neural network and long short term memory for service-oriented systems," Knowledge-Based Systems, vol. 159, pp.132-147, 2018.‏
  25. K. Bhuyan, D. P. Mohapatra, & S. Sethi, "Software Reliability Prediction using Fuzzy Min-Max Algorithm and Recurrent Neural Network Approach," International Journal of Electrical & Computer Engineering, vol. 6no. 4. Pp.2088-8708, 2016.
  26. Wang, & C. Zhang, "Software reliability prediction using a deep learning model based on the RNN encoder–decoder," Reliability Engineering & System Safety, vol.170, pp.73-82, 2018.
  27. Mittelman, "Time-series modeling with undecimated fully convolutional neural networks," arXiv preprint arXiv:1508.00317.‏2015.
  28. Binkowski, G. Marti, , & P. Donnat, "Autoregressive convolutional neural networks for asynchronous time series," In International Conference on Machine Learning pp. 580-589, PMLR.‏ 2018.
  29. N.rivastava, G. Hinton, A. Krizhevsky, I. Sutskever, &R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," Journal of machine learning research, vol.15,no.1,pp. 1929-1958,2014.
  30. Labach, H. Salehinejad, & S.Valaee, "Survey of dropout methods for deep neural networks," arXiv preprint arXiv:1904.13310,2019.
  31. Di Nunno, & F. Granata, "Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network," Environmental Research, vol.190,110062.‏2020.
  32. Di Nunno, G. de Marinis, R. Gargano, & F. Granata, " Tide prediction in the Venice Lagoon using Nonlinear Autoregressive Exogenous (NARX) neural network," Water, vol.13,no.9, 1173, 2021.‏
  33. Di Nunno, F. Granata, R. Gargano, & de Marinis, G. "Forecasting of extreme storm tide events using NARX neural network-based models," Atmosphere, vol.12, no.4, 512,2021.‏
  34. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V.K. Menon, &K. P. Soman, "Stock price prediction using LSTM, RNN and CNN-sliding window model," In 2017 international conference on advances in computing, communications and informatics (icacci)(pp. 1643-1647). September 2017.
  35. Srivastava, E. Mansimov, & R. Salakhudinov, "Unsupervised learning of video representations using lstms". In International conference on machine learning pp. 843-852, PMLR.,2015‏
  36. Wang, F. Yan, J. Lu, &W. Y. Yang, "COVID-19 Trend Forecasting by Using Dropout-LSTM Model," Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, vol.50,no.3, pp. 414-421, 2021.
  37. Tohma, H. Yamano, M. Ohba, & R. Jacoby, "Parameter estimation of the hyper-geometric distribution model for real test/debug data". In Proceedings. 1991 International Symposium on Software Reliability Engineering (pp. 28-29). IEEE Computer Society‏ 1991.
  38. Tohma, K. Tokunaga, S. Nagase, &Y. Murata, " Structural approach to the estimation of the number of residual software faults based on the hyper-geometric distribution, "IEEE transactions on software engineering, vol. 15,no. 3, pp. 345-355,1998.

International Journal of Reliability, Risk and Safety: Theory and Application
Volume 4, Issue 2
December 2021
Pages 1-12

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History

  • Receive Date: 21 September 2021
  • Revise Date: 30 October 2021
  • Accept Date: 07 March 2022

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