Dynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software

Lianfen Qian, Qingchuan Yao, Taghi M. Khoshgoftaar

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DOI: 10.4236/jsea.2010.36070                

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.

Keywords

Software Testing, Weekly Defect Arrival Data, Single-Phase Rayleigh Model, Two-Phase Truncated Rayleigh Model, Software Reliability

Conflicts of Interest

The authors declare no conflicts of interest.

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.