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A Metric-Set and Model Suggestion for Better Software Project Cost Estimation

Authors: Murat Ayyıldız, Oya Kalıpsız, Sırma Yavuz


Software project effort estimation is frequently seen as complex and expensive for individual software engineers. Software production is in a crisis. It suffers from excessive costs. Software production is often out of control. It has been suggested that software production is out of control because we do not measure. You cannot control what you cannot measure. During last decade, a number of researches on cost estimation have been conducted. The metric-set selection has a vital role in software cost estimation studies; its importance has been ignored especially in neural network based studies. In this study we have explored the reasons of those disappointing results and implemented different neural network models using augmented new metrics. The results obtained are compared with previous studies using traditional metrics. To be able to make comparisons, two types of data have been used. The first part of the data is taken from the Constructive Cost Model (COCOMO'81) which is commonly used in previous studies and the second part is collected according to new metrics in a leading international company in Turkey. The accuracy of the selected metrics and the data samples are verified using statistical techniques. The model presented here is based on Multi-Layer Perceptron (MLP). Another difficulty associated with the cost estimation studies is the fact that the data collection requires time and care. To make a more thorough use of the samples collected, k-fold, cross validation method is also implemented. It is concluded that, as long as an accurate and quantifiable set of metrics are defined and measured correctly, neural networks can be applied in software cost estimation studies with success

Keywords: Software Metrics, neural network, Software Cost Estimation

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[1] Devnani-Chulani, S.: Bayesian Analysis of Software Cost and Quality models. University of Southern California, Doctor of philosophy Thesis. (1999)
[2] Al-Sakran, H.: Software Cost Estimation Model Based on Integration of Multiagent and Case-Based Reasoning. Journal of Computer Science Volume 2(3) (2006) 276-282
[3] J├©rgensen M. and Shepperd M.: A Systematic Review of Software Development Cost Estimation Studies. IEEE Transactions on Software Engineering. (2006)
[4] Leung, H., Fan, Z.: In Handbook of Software Engineering and Knowledge Engineering (Ed,Chang, S. K.). Volume 2 World Scientific. (2002)
[5] Han, J. and Kamber, M.: Data mining concepts and techniques. Academic Press. San Francisco. (2001)
[6] Kan, S.H.: Metrics and Models in Software Quality Engineering. Adisson Wesley. (2002)
[7] Hughes, R.T.: An Evaluation of Machine Learning Techniques for Software Effort Estimation. University of Brighton. (1996)
[8] Wittig, G., Finnie, G.: Estimating Software Development Effort with Connectionist Models. Information and Software Technology. Volume 39 (1997) 469-476
[9] Venkatachalam, A.R.: Software Cost Estimation Using Artificial Neural Networks. International Joint Conference on Neural Networks. Nagoya. (1993)
[10] J├©rgensen, M.: Experience with the Accuracy of Software Main Task Effort Prediction Models. IEEE Transactions on Software Engineering, Volume 21(8) (1995) 674-681
[11] Serluca, C.: An Investigation Into Software Effort Estimation using a Back-Propogation Neural Network. M.Sc. Thesis. Bournemouth University. (1995)
[12] Briand L., Morasca S. and Basili V. (2002). An Operational process for goal-driven definition of measures. IEEE Transactions on Software Engineering, 30(2), 120-140.
[13] Zuse H. (1998) A Framework of Software Measurement. Walter de Gruyter Berlin.
[14] Fenton N. and Pfleeger S. (1997). Software Metrics: A Rigorous Approach. 2nd. edition. London. Chapman & Hall.
[15] Van Den Berg and Van Den Broek. (1996). Axiomatic Validation in the Software Metric Development Process. In Chapter 10: Software Measurement, Edited by Austin Melton, Thomson Computer Press.
[16] Weyuker E.J. (1988). Evaluating Software Complexity Measures. IEEE Transactions on Software Engineering. 14(9). 1357-1365.
[17] Whitmire S. (1997). Object Oriented Design Measurement. John Wiley & Sons. Inc.
[18] Reed, R. D. and Marks, R. J.: Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. MIT Press. (1999)
[19] Poels G. and Dedene G. (2000). Distance-based software measurement: necessary and sufficient properties for software measures. Information and Software Technology. 42(1). 35-46.
[20] Krantz D., Luce R.D., Suppes P. and Tversky A. (1971). Foundations of Measurement. Vol. 1. Academic Press. New York.
[21] Knuth E. D.: The art of computer programming. 2nd ed. Addison- Wesley. (1981)
[22] Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann. (1999)
[23] Briand, L., El Emam, K., Surmann, D., Wieczorek, I., and Maxwell, K.: An Assessment and Comparison of Common Software Cost Estimation Modeling Techniques. In Proceedings of the International Conference on Software Engineering. (1999) 313-322
[24] Idri A., Abran A., Khoshgoftaar T.: Fuzzy Case-Based Reasoning Models for Software Cost Estimation. Soft Computing in Software Engineering: Theory and Applications. Springer-Verlag. (2003)
[25] Witten, I. H. and Frank, E.: Data Mining: Practical machine learning tools and techniques. 2nd Ed. Morgan Kaufmann. San Francisco. (2005)