NDLI: Software reliability prediction model based on ICA algorithm and MLP neural network

Content Provider	IEEE Xplore Digital Library
Author	Noekhah, S. Hozhabri, A.A. Rizi, H.S.
Copyright Year	2013
Description	Author affiliation: Dept. of Manage., Univ. of Isfahan, Isfahan, Iran (Rizi, H.S.) \|\| Soft Comput. Res. Group (SCRG), Univ. Technol. Malaysia, Skudai, Malaysia (Noekhah, S.) \|\| Dept. of Manage., Univ. Teknol. Malaysia, Skudai, Malaysia (Hozhabri, A.A.)
Abstract	To achieve the high performance system without any failure, we should provide the high reliability level of software. Soft computing models for software reliability prediction suffer from low accuracy during predicting the number of faults. Moreover, the models have some problems like no solid mathematical foundation for analysis, being trapped in local minima, and convergence problem. This paper introduces Imperialist Competitive Algorithm (ICA) to overcome the weaknesses of previous models and improve the efficiency of training process of Multi-Layer Perceptron (MLP) neural network. Therefore, the network can predict the number of faults precisely. The results show that the proposed predicting model is more efficient than the existing techniques in prediction performance.
Starting Page	1
Ending Page	15
File Size	443161
Page Count	15
File Format	PDF
ISBN	9781479903948
e-ISBN	9781479903931
DOI	10.1109/ECDC.2013.6556733
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-17
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MLP Computational modeling Neural networks Software algorithms Predictive models Prediction algorithms Software Software reliability Neural network ICA algorithm
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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