IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 1 - Issue 1, February 2012 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Performance Analysis of RGMs by using Supervised Learning Techniques

[Full Text]

 

AUTHOR(S)

Y Vamsidhar, P Samba Siva Raju, T Ravi Kumar

 

KEYWORDS

Software Reliability, Software Reliability Growth Models (SRGMs), AdaBoosting Algorithm, Least Square Estimation, Maximum Likelihood Estimation.

 

ABSTRACT

Software reliability is one of a number of aspects of computer software which can be taken into consideration when determining the quality of the software. Building good reliability models is one of the key problems in the field of software reliability. A good software reliability model should give good predictions of future failure behavior, compute useful quantities and be widely applicable. Software Reliability Growth Models (SRGMs) are very important for estimating and predicting software reliability. An ideal SRGM should provide consistently accurate reliability estimation and prediction across different projects. However, that there is no single such model which can obtain accurate results for different cases. The reason is that the performance of SRGMs highly depends on the assumptions on the failure behavior and the application data-sets. In other words, many models may be shown to perform well with one failure data-set, but bad with the other data-set. Thus, combining some individual SRGMs than single model is helpful to obtain more accurate estimation and prediction. SRGM parameters are estimated using the least square estimation (LSE) or Maximum Likelihood Estimation (MLE). Several combinational methods of SRGMs have been proposed to improve the reliability estimation and prediction accuracy. The AdaBoosting algorithm is one of the most popular machine learning algorithms. An AdaBoosting based Combinational Model (ACM) is used to combine the several models. The key idea of this approach is that we select several SRGMs as the weak predictors and use AdaBoosting algorithm to determine the weights of these models for obtaining the final linear combinational model. In this paper, the Fitness and Prediction of various Software Reliability Growth Models (SRGMs) can be compared with AdaBoosting based Combinational Model (ACM) with the help of Maximum likelihood estimation to estimate the model parameters.

 

REFERENCES

[1] Hoang Pham, System Software Reliability. Springer Series in Reliability Engineering..
[2] Jiri Matas and Jan S ochman, AdaBoost, Centre for Machine Perception, Czech Technical University, Prague.
[3] Haifeng Li, Min Zeng, and Minyan Lu, "Exploring AdaBoosting Algorithm for Combining Software Reliability Models",ISSRE 2009.
[4] X. Cai, M. R. Lyu. Software Reliability Modeling with Test Coverage Experimentation and Measurement with a Fault-Tolerant Software Project. ISSRE, 2007: 17-26
[5] C. Y. Huang, S. Y. Kuo and M. R. Lyu. An assessment of testing-effort dependent software reliability growth models. IEEE Transactions on Reliability, 2007, 56(2): 198-211
[6] Lyu, M. R, Nikora, A. Applying Reliability Models More Effective. IEEE Software, 1992, 9(4): 43-52
[7] Y. S. Su, C. Y. Huang. Neural-network based approaches for software reliability estimation using dynamic weighted combinational models. The Journal of Systems and Software, 2007, 80: 606-615
[8] C. J. Hsu, C. Y. Huang. Reliability analysis using weighted combinational models for web-based software. WWW 2009, 1131-1132
[9] Eduardo Oliveira Costa, Silvia R. Vergilio, Aurora Pozo, Gustavo Souza. Modeling software reliability growth with Genetic Programming. ISSRE, 2005: 1-10
[10]Artur Ferreira, Survey on Boosting algorithms for supervised and semi-supervised learning. Institute of Telecommunications.
[11] Aasia Quyoum, Mehraj - Ud - Din Dar, Improving Software Reliability using Software Engineering Approach, International Journal of Computer Applications (0975 - 8887) Volume 10- No.5, November 2010.

[12] S. Yamada, J. Hishitani, and S. Osaki, "Software reliability growth model with Weibull testing effort: a model and application" , IEEE Trans. Reliability, vol. R-42, pp. 100-105, 1993.
[13]Alan Wood, "Software reliability growth models". Tandem Computers.