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Factor Analysis in Software Defect Prediction

Swathi. K, Arun Birader

Abstract


Reliance on the product framework by a person is expanding radically. Because of this reliance, higher unwavering quality in the product items is required. Henceforth, programming designers are being compelled to give more consideration regarding enhancing programming item quality with respect to dependability. Imperfection thickness is a parameter utilized by the designer to quantify the unwavering quality of the item before its formal discharge. Effective and early forecast of imperfection thickness empowers a product engineer to recognize conceivable enhancements in the item being worked on. It additionally persuades the improvement group to disperse the assets adequately in the advancement of various parts of the item. In this Paper , we do Factor Analysis of our Questionnaire Variables to recognize the components which are essential.


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References


Clark, B. and Zubrow, D., “How Good Is the Software: A Review of Defect Prediction Techniques”, Software Engineering Institute, SEPG 2002 Conference.

N. Fenton and M. Neil “A Critique of Software Defect Prediction Research”, IEEE Trans. Software Eng., 25, No.5, 1999.

Du Zhang, “Applying Machine Learning Algorithms in Software Development” The Proceedings of 2000 Monterey Workshop on Modeling Software System Structures, Santa Margherita Ligure, Italy, pp. 275-285.

L. Guo, Y. Ma, B. Cukic, H. Singh, “Robust prediction of fault proneness by random forests,” In: Proceedings of the 15th International Symposium on Software Reliability Engineering (ISSRE‟04), pp. 417–428, 2004.

T.M. Khoshgaftaar, E.D. Allen, J.P, Hudepohl, S.J. Aud, Application of neural networks to software quality modeling of a very large telecommunications system,” IEEE Transactions on Neural Networks, vol. 8, no. 4, pp. 902- 909, 1997.

Norman Fenton, Paul Krause and Martin Neil, “A Probabilistic Model for Software Defect Prediction”, IEEE Transactions on Software Engineering, 2001.

K. Elish, M. Elish, “Predicting defect-prone software modules using support vector machines,” Journal of System and Software, vol. 81, pp. 649-660.

T. Mitchell, Machine Learning, McGraw-Hill, 1997.

F.V. Jensen, “An Introduction to Bayesian Networks”, Springer, 1996.


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