NDLI: Evolutionary regression prediction for software cumulative failure modeling: A comparative study

Content Provider	IEEE Xplore Digital Library
Author	Benaddy, M. Wakrim, M. Aljahdali, S.
Copyright Year	2009
Description	Author affiliation: Dept. of Math. & Info. Equipe MMS, Ibn Zohr University Morocco (Benaddy, M.; Wakrim, M.) \|\| Taif University Saudi Arabia (Aljahdali, S.)
Abstract	An evolutionary regression modeling approach for software cumulative failure prediction based on auto-regression order 4, 7 and 10 models are proposed. A real coded genetic algorithm is used to optimize the mean square of the error produced by training the auto-regression model. In this paper, we present a real coded genetic algorithm that uses the appropriate operators for this encoding type to train the auto-regression model. To evaluate the predictive capability of the developed model data sets, various projects were used. A comparison between auto-regression order 4 model trained using least square estimation [1] and real coded genetic algorithm training is provided, also a comparison between the auto-regression order 7 and 10 models trained using the genetic algorithm is presented. Experimental results show that the training of different auto-regression model by the real coded genetic algorithm has a good predictive capability.
Starting Page	286
Ending Page	292
File Size	1819028
Page Count	7
File Format	PDF
ISBN	9781424437566
DOI	10.1109/MMCS.2009.5256687
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-02
Publisher Place	Morocco
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Genetic algorithms Software reliability Application software Least squares approximation Encoding Parameter estimation Telephony Laboratories Condition monitoring Software Reliability Genetic Algorithms Real Coded Genetic Algorithms Auto Regression Model
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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