Cited By
View all- Wu CHuang C(2021)A Study of Incorporation of Deep Learning Into Software Reliability Modeling and AssessmentIEEE Transactions on Reliability10.1109/TR.2021.310553170:4(1621-1640)Online publication date: Dec-2021
Measures for Predicting Software Reliability using Time Recurrent Neural Networks with Back-propagation
Pages 1 - 8
Abstract
Recurrent Neural Network (RNN) has been known to be very useful in predicting software reliability. A number of parametric models and reliability growth models, have been proposed, but developing a model that can predict reliability in all types of data sets, in any environment, and at any phase of software development is still a challenge. In this paper, we propose a model that explores the applicability of Recurrent Neural Network with Back- propagation Through Time (RNNBPTT) learning rule to predict software reliability. The detailed procedure of reliability prediction using recurrent neural networks is explained. The model has been applied on data sets collected across several standard software projects during system testing phase with fault removal. Though the procedure is relatively complicated, the results depicted in this work suggest that Fully Recurrent Neural Networks (FRNN) exhibits an accurate and consistent behavior in reliability prediction.
References
[1]
Akpan, V. A., and Hassapis, G. D. Adaptive recurrent neural network training algorithm for nonlinear model identification using supervised learning. In American Control Conference (ACC) (Baltimore, MD, July 2010), American Control Conference, IEEE, pp. 4937--4942.
[2]
Boland, P. J. Challenges in Software Reliability and Testing. Technical report, Department of Statistics National University of Ireland, Dublin Belfield -- Dublin 4 Ireland, 2002.
[3]
Costa, E. O., R., S., Aurora, V., and Souza, P. G., Eds. Modeling Software Reliability Growth with Genetic Programming (Chicago, Illinois, November 2005), Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering.
[4]
Hush, D. R., and Herne, B. G. Progress in supervised neural networks. Signal Processing Magazine, IEEE 10, 1 (January 1993), 8--39.
[5]
Hush, D. R., Horne, B. G., and Salas, J. M. Error surfaces for multi-layer perceptrons. Systems, Man and Cybernetics, IEEE Transactions on 22, 5 (September 1992), 1152--1161.
[6]
IEEE. Standard glossary of software engineering terminology. Standards Coordinating Committee of the IEEE Computer Society, 1991.
[7]
Iyer, R. K., and Lee, I. Measurement-based analysis of software reliability, Handbook of Software Reliability Engineering. McGraw-Hill, 1996, pp. 303--358.
[8]
Karunanithi, N., Malaiya, Y., and Whitley, D. Prediction of Software Reliability Using Neural Networks. In Proceedings IEEE International Symposium on Software Reliability Engineering (Austin, TX, May 1991), IEEE, pp. 124--130.
[9]
Karunanithi, N., and Whitley, D. Prediction of Software Reliability Using Feed-forward and Recurrent Neural Nets. In Neural Networks, 1992. IJCNN (Baltimore, MD, June 1992), vol. 1, IEEE, pp. 800--805.
[10]
Karunanithi, N., Whitley, D., and Malaiya, Y. K. Prediction of Software Reliability Using Connectionist Models. IEEE Trans. Software Eng. 18, 7 (July 1992), 563--574.
[11]
Karunanithi, N., Whitley, D., and Malaiya, Y. K. Using Neural Networks in Reliability Prediction. IEEE Software 9, 4 (July 1992), 53--59.
[12]
Khatatneh, K., and Mustafa, T. Software Reliability Modeling using Soft Computing Technique. European Journal of Scientific Research 26, 1 (2009), 154--160.
[13]
Khoshgoftaar, T. M., Pandya, A. S., and More, H. A Neural Network Approach For Predicting Software Development Faults. Proceedings of Third International Symposium on Software Reliability Engineering, pp. 83--89.
[14]
Lin, C.-T., and Lee, C. G. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall, Inc, May 1996.
[15]
Lo, J. H. The Implementation of Artificial Neural Networks Applying to Software Reliability Modeling. Control and Decision Conference, 2009. CCDC '09. nChinese (June 2009), 4349--4354.
[16]
Maeda, Y., and Wakamura, M. Simultaneous perturbation learning rule for recurrent neural networks and its fpga implementation. IEEE TRANSACTIONS ON NEURAL NETWORKS 16, 6 (NOVEMBER 2005), 1664--1672.
[17]
Malaiya, Y. K., Karunanithi, N., and Verma, P. Predictability of software reliability models. IEEE Transactions on Reliability 41, 4 (December 1992), 539--546.
[18]
Mazumdar, J., and Harley, R. G. Recurrent Neural Networks Trained With Backpropagation Through Time Algorithm to Estimate Nonlinear Load Harmonic Currents. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 55, 9 (September 2008), 3484--3491.
[19]
Mohanty, R., Ravi, V., and Patra, M. R. Hybrid Intelligent Systems for Predicting Software Reliability. Applied Soft Computing 13, 1 (August 2013), 189--200.
[20]
Musa, J. D. Software Reliability Data. Data & Analysis Center for Software, January 1980.
[21]
Musa, J. D., and Okumoto, K. A Logarithmic Poisson Execution Time Model for Software Reliability Measurement. In ICSE (NJ, USA, 1984), EEE Press Piscataway, Proceedings of the 7th International Conference on software Engineering, pp. 230--238.
[22]
RajKiran, N., and Ravi, V. Software Reliability Prediction using Wavelet Neural Networks. In International Conference on Computational Intelligence and Multimedia Applications (Sivakasi, Tamil Nadu, December 2007), vol. 1, IEEE, pp. 195--199.
[23]
RajKiran, N., and Ravi, V. Software Reliability Prediction by Soft Computing Techniques. Journal of Systems and Software 81, 4 (April 2008), 576--583.
[24]
Singh, Y., Kaur, A., and Malhotra, R. Empirical Validation of Object-Oriented Metrics for Predicting Fault Proneness Models. Journal Software Quality Control archive, Springer Science Business Media, LLC 18, 1 (July 2009), 3--35.
[25]
Singh, Y., and Kumar, P. Prediction of Software Reliability Using Feed Forward Neural Networks. In Computational Intelligence and Software Engineering (CiSE) (2010), I. Conference, Ed., IEEE, pp. 1--5.
[26]
Sitte, R. Comparison of software-reliability-growth predictions: neural networks vs parametric-recalibration. Reliability, IEEE Transactions 48, 3 (September 1999), 285--291.
[27]
Sivanandam, S. N., and Deepa, S. Principles of Soft Computing, 1st ed. Wiley India (P) Ltd., Ansari Road Daryaganj, New Delhi, April 2007).
[28]
Thwin, M. M. T., and Quah, T. S., Eds. Application of Neural Network for Predicting Software Development Faults using Object-Oriented Design Metrics (November 2002), vol. 5, Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02).
[29]
Tian, L., and Noore, A. Software Reliability Prediction Using Recurrent Neural Network With Bayesian Regularization. International Journal of Neural Systems 14, 3 (Jun 2004), 165--174.
[30]
Tian, L., and Noore, A. On-line Prediction of Software Reliability using an Evolutionary Connectionist Model. ScienceDirect,The Journal of Systems and Software 77, 2 (August 2005), 173--180.
[31]
Werbos, P. J., Ed. Backpropagation through time: What does it does and how to do it (Nat. Sci. Found., Washington, DC, USA, October 1990), vol. 78, Proceedings of the IEEE.
[32]
Zhao, L., pei Zhang, J., Yang, J., and Chu, Y. Software reliability growth model based on fuzzy wavelet neural network. In 2nd International Conference on Future Computer and Communication (ICFCC)(Wuhan, May 2010), vol. 1, IEEE, pp. 664--668.
Index Terms
- Measures for Predicting Software Reliability using Time Recurrent Neural Networks with Back-propagation
Recommendations
Software failure time series prediction with RBF, GRNN, and LSTM neural networks
AbstractThe important task of software quality assurance is failure prediction. Time series forecasting methods can be successfully used for this purpose. This paper aims to study and compare the effectiveness of software failure prediction using ...
On the adoption of neural networks in modeling software reliability
ESEC/FSE 2018: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software EngineeringThis work models the reliability of software systems using recurrent neural networks with long short-term memory (LSTM) units and truncated backpropagation algorithm, and encoder-decoder LSTM architecture and proposes LSTM with software reliability ...
Physical time series prediction using Recurrent Pi-Sigma Neural Networks
This paper presents a new type of recurrent neural network, called the Recurrent Pi-Sigma Neural Network (RPSN) and its application to physical time series prediction. The network is constructed of two layers, the sigma and the pi unit layers. The ...
Comments
Information & Contributors
Information
Published In
September 2015
67 pages
Copyright © 2015 Authors.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 September 2015
Published in SIGSOFT Volume 40, Issue 5
Check for updates
Author Tags
Qualifiers
- Review-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 201Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
Cited By
View all- Wu CHuang C(2021)A Study of Incorporation of Deep Learning Into Software Reliability Modeling and AssessmentIEEE Transactions on Reliability10.1109/TR.2021.310553170:4(1621-1640)Online publication date: Dec-2021
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in