Comparison of ANFIS with MLP ANN in Measuring the Reliability based on Aspect Oriented Software
Автор: Mohammad Zavvar, Farhad Ramezani
Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs
Статья в выпуске: 9 vol.7, 2015 года.
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In fact, Reliability as the qualities metric is the probability success or The probability that a system or set of tasks without failure for a specified constraints of time and space, as specified in the design and operating conditions specified temperature, humidity, vibration and action. A relatively new methodologies for developing complex software systems engineering is an aspect-oriented software systems, that provides the new methods for the separation of concerns multiple module configuration or intervention and automatic integration them with a system. In this paper, using MLP artificial neural networks and adaptive fuzzy neural network assess the reliability of the aspect oriented software and at the end, two methods were compared with each other. After examination, the root means square error method based on artificial neural networks, fuzzy neural network-based method of 0.024262 and 0.021874 to be adaptive. The results show that the method is based on adaptive fuzzy neural networks with low error in the estimation of reliability, performance is better than the MLP artificial neural network approach.
Adaptive Fuzzy Neural Network, Artificial Neural Network, Reliability, Aspect Oriented Software
Короткий адрес: https://sciup.org/15014792
IDR: 15014792
Список литературы Comparison of ANFIS with MLP ANN in Measuring the Reliability based on Aspect Oriented Software
- Seyster, J., et al. Aspect-oriented instrumentation with GCC. in Runtime Verification. 2010. Springer.
- Singh, P.K. and O.P. Sangwan, A Framework for Assessing the Software Reusability using Fuzzy Logic Approach for Aspect Oriented Software. 2015.
- Singh, P.K., et al., An Assessment of Software Testability using Fuzzy Logic Technique for Aspect-Oriented Software. International Journal of Information Technology and Computer Science (IJITCS), 2015. 7(3): p. 18.
- Cleveland, J., J. Loyall, and J. Hanna. An Aspect-Oriented Approach to Assessing Fault Tolerance. in Military Communications Conference (MILCOM), 2014 IEEE. 2014. IEEE.
- Qian, Z., H. Yu, and G. Fan, Modeling of Adaptive Cyber Physical Systems using Aspect-oriented Approach. Appl. Math, 2015. 9(4): p. 1981-1992.
- Cardoso, J.M., et al. LARA: an aspect-oriented programming language for embedded systems. in Proceedings of the 11th annual international conference on Aspect-oriented Software Development. 2012. ACM.
- Mehner-Heindl, K., M. Monga, and G. Taentzer, Analysis of aspect-oriented models using graph transformation systems, in Aspect-Oriented Requirements Engineering. 2013, Springer. p. 243-270.
- Molderez, T. and D. Janssens, Modular Reasoning in Aspect-Oriented Languages from a Substitution Perspective, in Transactions on Aspect-Oriented Software Development XII. 2015, Springer. p. 3-59.
- Mordal, K., et al., Software quality metrics aggregation in industry. Journal of Software: Evolution and Process, 2013. 25(10): p. 1117-1135.
- Ampatzoglou, A., G. Frantzeskou, and I. Stamelos, A methodology to assess the impact of design patterns on software quality. Information and Software Technology, 2012. 54(4): p. 331-346.
- Chiu, K.-C., Y.-S. Huang, and T.-Z. Lee, A study of software reliability growth from the perspective of learning effects. Reliability Engineering & System Safety, 2008. 93(10): p. 1410-1421.
- Pham, T.-T., X. Défago, and Q.-T. Huynh, Reliability prediction for component-based software systems: Dealing with concurrent and propagating errors. Science of Computer Programming, 2015. 97: p. 426-457.
- Kiran, N.R. and V. Ravi, Software reliability prediction by soft computing techniques. Journal of Systems and Software, 2008. 81(4): p. 576-583.
- Zheng, Z. and M.R. Lyu. Collaborative reliability prediction of service-oriented systems. in Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1. 2010. ACM.
- Figueiredo, E., et al, Assessing aspect-oriented artifacts: Towards a tool-supported quantitative method. 2005.
- Sant'Anna, C., et al. On the reuse and maintenance of aspect-oriented software: An assessment framework. in Proceedings of Brazilian symposium on software engineering. 2003.
- Reena Dadhich, B.M., Measuring Reliability of an Aspect Oriented Software Using Fuzzy Logic Approach. International Journal of Engineering and Advanced Technology (IJEAT), June 2012.
- Gupta, N., Artificial neural network. Network and Complex Systems, 2013. 3(1): p. 24-28.
- Yetilmezsoy, K. and S. Demirel, Artificial neural network (ANN) approach for modeling of Pb (II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells. Journal of Hazardous Materials, 2008. 153(3): p. 1288-1300.
- Melin, P., et al., A new approach for time series prediction using ensembles of ANFIS models. Expert Systems with Applications, 2012. 39(3): p. 3494-3506.
- Chai, T. and R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific Model Development Discussions, 2014. 7: p. 1525-1534.
- Oseni, O.F., et al., Comparative Analysis of Received Signal Strength Prediction Models for Radio Network Planning of GSM 900 MHz in Ilorin, Nigeria. 2014.
- Ronquist, F., et al., MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic biology, 2012. 61(3): p. 539-542.
