A New Empirical Model to Increase the Accuracy of Software Cost Estimation (TECHNICAL NOTE)

Authors

Department of Computer Science, BIT, Mesra, Ranchi, India

Abstract

We can say a software project is successful when it is delivered on time, within the budget and maintaining the required quality. However, nowadays software cost estimation is a critical issue for the advance software industry. As the modern software’s behaves dynamically so estimation of the effort and cost is significantly difficult. Since last 30 years, more than 20 models are already developed to estimate the effort and cost for the betterment of software industry. Nevertheless, these algorithms cannot satisfy the modern software industry due to the dynamic behavior of the software for all kind of environments. On this study, an empirical interpolation model is developed to estimate the effort of the software projects. This model compares with the COCOMO based equations and predicts its result analyzing individually taking different cost factors. The equation consists one independent variable (KLOC) and two constants a, b which are chosen empirically taking different NASA projects historical data and the results viewed in this model are compared with COCOMO model with different values of scale factor. In this paper the author analyze more than 250 projects collected from PROMISE repository. The effort variance is very low and the proposed model has the lowest Mean Magnitude of Relative Error (MMRE) and RMSSE.

Keywords

References

1.     Seth, K. and Sharma, A., "Effort estimation techniques in component based development-a critical review proceedings of the 3rd national conference", INDIACom-2009, New Delhi, India.
2.     Shepperd, M. and Schofield, C., "Estimating software project effort using analogies", IEEE Transactions on Software Engineering,  Vol. 23, No. 11, (1997), 736-743.
3.     Maxwell, K.D. and Forselius, P., "Benchmarking software development productivity", Ieee Software,  Vol. 17, No. 1, (2000), 80-88.
4.     Molokken, K. and Jorgensen, M., "A review of software surveys on software effort estimation", in Empirical Software Engineering. ISESE 2003. Proceedings. 2003 International Symposium on, IEEE., (2003), 223-230.
5.     Boehm, B.W., "Software engineering economics, Prentice-hall Englewood Cliffs (NJ),  Vol. 197,  (1981).
6.     Srivastava, D.K., Chauhan, D.S. and Singh, R., "Square model-a software process model for ivr software system".
7.     Jorgensen, M. and Sjoberg, D.I., "The impact of customer expectation on software development effort estimates", International Journal of Project Management,  Vol. 22, No. 4, (2004), 317-325.
8.     Uysal, M., "Estimation of the effort component of the software projects using simulated annealing algorithm",  New Trends in Technologies, DOI: 10.5772/7583, (2008).
9.     Boehm, B.W., "Understanding and controlling software costs", Journal of Parametrics,  Vol. 8, No. 1, (1988), 32-68.
10.   Attarzadeh, I. and Ow, S.H., "A novel soft computing model to increase the accuracy of software development cost estimation", in Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on, IEEE. Vol. 3, (2010), 603-607.
11.   Deshpande, M. and Bhirud, S., "Analysis of combining software estimation techniques", International Journal of Computer Applications (0975–8887), Vol. 5, No. 3, (2010), DOI: 10.5120/901-1277.
12.   Bailey, J.W. and Basili, V.R., "A meta-model for software development resource expenditures", in Proceedings of the 5th international conference on Software engineering, IEEE Press., (1981), 107-116.
13.   Benediktsson, O. and Dalcher, D., "Effort estimation in incremental software development", IEE Proceedings-Software,  Vol. 150, No. 6, (2003), 351-357.
14.   Ledesma, S., Avina, G. and Sanchez, R., Practical considerations for simulated annealing implementation, in Simulated annealing. 2008, InTech.
15.   Suri, P., Bhushan, B. and Jolly, A., "Time estimation for project management life cycle: A simulation approach", International Journal of Computer Science and Network Security,  Vol. 9, No. 5, (2009), 211-215.
16.   Ali, M., Torn, A. and Viitanen, S., A direct search simulated annealing algorithms for optimization involving continuous variables. (1997), Technical report, Turku Centre for Computer Science, Abo Akademi University, Finland.
17.   Goel, T. and Stander, N., "Adaptive simulated annealing for global optimization in ls-opt", in Proceedings of the 7th European LS-DYNA Conference. California: LSTC., (2009), 1-8.
18.   Pressman, R.S., "Software engineering: A practitioner's approach, Palgrave Macmillan, McGraw-Hill Education; 8 edition, ISBN-10: 0078022126, (2005).
19.   Jalote, P., "An integrated approach to software engineering", Springer Science & Business Media, ISBN 978-0-387-28132-2, (2012).
International Journal of Engineering
Volume 30, Issue 10
TRANSACTIONS A: Basics
October 2017
Pages 1487-1493

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