Comparative Study of the Performance of M5-Rules Algorithm with Different Algorithms

Heetika Duggal, Parminder Singh

Department of Information Technology, Chandigarh Engineering College, Mohali, India.
Department of Information Technology, Chandigarh Engineering College, Mohali, India..

DOI: 10.4236/jsea.2012.54032                

Abstract

The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The determination of the value of this variable when initiating software projects allows us to plan adequately any forthcoming activities. As far as estimation and prediction is concerned there is still a number of unsolved problems and errors. To obtain good results it is essential to take into consideration any previous projects. Estimating the effort with a high grade of reliability is a problem which has not yet been solved and even the project manager has to deal with it since the beginning. In this study, performance of M5-Rules Algorithm, single conjunctive rule learner and decision table majority classifier are experimented for modeling of Effort Estimation of Software Projects and performance of developed models is compared with the existing algorithms namely Halstead, Walston-Felix, Bailey-Basili, Doty in terms of MAE and RMSE. The proposed techniques are run in the WEKA environment for building the model structure for software effort and the formulae of existing models are calculated in the MATLAB environment. The performance evaluation criteria are based on MAE and RMSE. The result shows that the M5-Rules have the best performance and can be used for the effort estimation of all types of software projects.

Keywords

Software Cost Estimation; Effort Estimation; Effort Estimation Models; Rule Generation; COCOMO Model; Conjunctive Rule Learner; Decision Table; M5-Rules Learner

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	B. W. Boehm, “Software Engineering Economics,” 1st Edition, Prentice-Hall, Englewood Cliffs, 1981.
[2]	S. Price, 2007. http://www.pricesystems.com.
[3]	G. Cantone, A. Cimitile and U. De Carlini, “A Comparison of Models for Software Cost Estimation and Management of Software Projects,” In: Computer Systems: Performance and Simulation, Elsevier Science, Amsterdam, 1986, pp. 123-140.
[4]	L. H. Putnam, “A General Empirical Solution to the Macro Software Sizing and Estimating Problem,” IEEE Transactions on Software Engineering, Vol. SE-4, No. 4, 1978, pp. 345-361. doi:10.1109/TSE.1978.231521
[5]	N. A. Parr, “An Alternative to the Raleigh Curve Model for Software Development Effort,” IEEE Transactions on Software Engineering, 1980, pp. 77-85.
[6]	P. S. Sandhu, M. Prashar, P. Bassi and A. Bisht, “A Model for Estimation of Efforts in Development of Software Systems,” World Academy of Science, Engineering and Technology, Vol. 56, 2009.
[7]	W. Royce, “Software Project Management: A Unified Framework,” Addison Wesley, Boston, 1998.
[8]	B. W. Boehm, et al., “The COCOMO 2.0 Software Cost Estimation Model,” American Programmer, 1996, pp. 217.
[9]	C. E. Walston and C. P. Felix, “A Method of Programming Measurement and Estimation,” IBM Systems Journal, Vol. 16, No. 1, 1977, pp. 54-73. doi:10.1147/sj.161.0054
[10]	J. Albrecht and J. E. Gaffney, “Software Function, Source Lines of Codes, and Development Effort Prediction: A Software Science Validation,” IEEE Transactions on Software Engineering, Vol. SE-9, No. 6, pp. 639-648. doi:10.1109/TSE.1983.235271
[11]	M. H. Halstead, “Elements of Software Science,” Elsevier, New York, 1977.
[12]	Doty Associates, Inc., “Software Cost Estimates Study,” Vol. 1, 1977, pp. 77-220.
[13]	J. W. Bailey and V. R. Basili, “A Meta-Model for Soft Ware Development Resource Expenditures,” Proceedings of the 5th International Conference on Software Engineering, 1981, pp. 107-116.
[14]	J. Baik, B. Boehm and B. Steece, “Disaggregating and Calibrating the CASE Tool Variable in COCOMO II,” IEEE Transactions on Software Engineering, Vol. 28, No. 11, 2002, pp. 1009-1022. doi:10.1109/TSE.2002.1049401
[15]	S. Devnani-Chulani, B. Clark and B. Boehm, “Calibration Results of COCOMO II.1997,” 22nd Annual Software Engineering Workshop, NASA Goddard Space Flight Center, 1997.
[16]	V. U. B. Challagulla, F. B. Bastani, I.-L. Yen and R. A. Paul, “Empirical Assessment of Machine Learning Based Software Defect Prediction Techniques,” 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems, Sedona, 2-4 February 2005, pp. 263-270. doi:10.1109/WORDS.2005.32
[17]	WEKA, 2007. http://www.cs.waikato.ac.nz/~ml/weka