Back
 JSEA  Vol.3 No.6 , June 2010
Dynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software
Abstract: Software reliability modeling and prediction are important issues during software development, especially when one has to reach a desired reliability prior to software release. Various techniques, both static and dynamic, are used for reliability modeling and prediction in the context of software risk management. The single-phase Rayleigh model is a dynamic reliability model; however, it is not suitable for software release date prediction. We propose a new multi-phase truncated Rayleigh model and obtain parameter estimation using the nonlinear least squares method. The proposed model has been successfully tested in a large software company for several software projects. It is shown that the two-phase truncated Rayleigh model outperforms the traditional single-phase Rayleigh model in modeling weekly software defect arrival data. The model is useful for project management in planning release times and defect management.
Cite this paper: nullL. Qian, Q. Yao and T. Khoshgoftaar, "Dynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software," Journal of Software Engineering and Applications, Vol. 3 No. 6, 2010, pp. 603-609. doi: 10.4236/jsea.2010.36070.
References

[1]   M. R. Lyu, “Software Reliability: To Use or not to Use?” Proceedings of 5th International Symposium on Soft- ware Reliability Engineering, 66-73 November 1994.

[2]   Y. Wang and M. Smith, “Release Date Prediction for Telecommunication Software Using Bayesian Belief Networks,” Proceedings of the 2002 IEEE Canadian Conference on Electrical and Computer Engineering, 2002, pp. 738-742.

[3]   T. M. Khoshgoftaar and N. Seliya, “Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques,” Empirical Software Engineering Journal, Vol. 8, No. 3, 2003, pp. 255-283.

[4]   T. M. Khoshgoftaar and N. Seliya, “Comparative Asse- ssment of Software Quality Classification Techniques: An Empirical Case Study,” Empirical Software Engin- eering Journal, Vol. 9, No. 3, 2004, pp. 229-257.

[5]   M. Thangarajan and B. Biswas, “Mathematical Model for Defect Prediction across Software Development Life Cycle,” The SEPG (Software Engineering Process Group) Conference, India, 2000. http://www.qaiindia. com/Conferences/SEPG2000/index.html

[6]   S. H. Kan, “Metric and Models in Software Quality Engineering,” 2nd Edition, Addison Wesley, Massa- chusetts, 2003.

[7]   P. V. Norden, “Useful Tools for Project Management,” Operations Research in Research and Development, B. V. Dean, Ed., John Wiley & Sons, New York, 1963.

[8]   L. H. Putman, “A General Empirical Solution to the Macro Software Sizing and Estimating Problem,” IEEE Transaction on Software Engineering, Vol. SE-4, 1978, pp. 345-361.

[9]   S. K. Bhattacharya and R. K. Tyagi, “Bayesian Survival Analysis Based on the Rayleigh Model,” Trabajos de Estadistica, Vol. 5, No. 1, 1990, pp. 81-92.

[10]   D. M. Bates and J. M. Chambers, “Nonlinear Models,” Chapter 10 of Statistical Models in S. J. M. Chambers and T. J. Hastie, Eds., Wadsworth & Brooks/Cole, 1992.

 
 
Top