International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 10, No. 3, 8 Mar. 2018
Cover page and Table of Contents: PDF (size: 773KB)
Machine Learning Application to Improve COCOMO Model using Neural Networks
Full Text (PDF, 773KB), PP.35-51
Views: 0 Downloads: 0
Author(s)
Somya Goyal
1,*
Anubha Parashar
2
Index Terms
COCOMO (Constructive Cost Model), Correlation, Machine Learning, MMRE (Mean Magnitude of Relative Error), Neural Network, Software Effort Estimation
Abstract
Millions of companies expend billions of dollars on trillions of software for the development and maintenance. Still many projects result in failure causing heavy financial loss. Major reason is the inefficient effort estimation techniques which are not so suitable for the current development methods. The continuous change in the software development technology makes effort estimation more challenging. Till date, no estimation method has been found full-proof to accurately pre-compute the time, money, effort (man-hours) and other resources required to successfully complete the project resulting either over-estimated budget or under-estimated budget. Here a machine learning COCOMO is proposed which is a novel non-algorithmic approach to effort estimation. This estimation technique performs well within their pre-specified domains and beyond so. As development methods have undergone revolutionaries but estimation techniques are not so modified to cope up with the modern development skills, so the need of training the models to work with updated development methods is being satiated just by finding out the patterns and associations among the domain specific data sets via neural networks along with carriage of desired COCOMO features. This paper estimates the effort by training proposed neural network using already published data-set and later on, the testing is done. The validation clearly shows that the performance of algorithmic method is improved by the proposed machine learning method.
Cite This Paper
Somya Goyal, ANUBHA Parashar, "Machine Learning Application to Improve COCOMO Model using Neural Networks", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.3, pp.35-51, 2018. DOI:10.5815/ijitcs.2018.03.05
Reference
[1]Boehm, B. W., “Software Engineering Economics”, IEEE Transactions on Software Engineering, SE-1O, 1, pp. 4-21, January 1984.
[2]Boehm, B. W., “Software Engineering Economics”, Prentice-Hall Inc., Englewood Cliffs, NJ, 1981.
[3]Chiu NH, Huang SJ, “The Adjusted Analogy-Based Software Effort Estimation Based on Similarity Distances”, Journal of Systems and Software, Vol. 80, No. 4, pp. 628-640, 2007.
[4]Jorgen M., Sjoberg D.I.K, “The Impact of Customer Expectation on Software Development Effort Estimates”, International Journal of Project Management, Elsevier, pp. 317-325, 2004.
[5]Jeffery R., Ruhe M., Wieczorek I., “Using Public Domain Metrics to Estimate Software Development Effort”, In Proceedings of the 7th International Symposium on Software Metrics, IEEE Computer Society, pp. 1627, 2001.
[6]Kaczmarek J., Kucharski M., “Size and Effort Estimation for Applications Written in Java”, Journal of Information and Software Technology, Vol. 46, No. 9, pp. 589-60, 2004.
[7]Heiat A., “Comparison of Artificial Neural Network and Regression Models for Estimating Software Development Effort”, Journal of Information and Software Technology, Vol. 44, No. 15, pp. 911-922, 2002.
[8]Srinivasan K. and Fisher D., “Machine Learning Approaches to Estimating Software Development Effort”, IEEE Transactions on Software Engineering, Vol. 21, pp. 126-137, 1995.
[9]Venkatachalam A.R., “Software Cost Estimation Using Artificial Neural Networks”, In Proceedings of the International Joint Conference on Neural Networks, 1993.
[10]Selby R.W. and Porter A.A., “Learning from Examples-Generation and Evaluation of Decision Trees for Software Resource Analysis”, IEEE Transactions on Software Engineering, Vol. 14, pp. 1743-1757, 1988.
[11]Subramanian G.H., Pendharkar P.C. and Wallace M., An Empirical Study of the Effect of Complexity, Platform, and Program Type on Software Development Effort of Business Applications, Empirical Software Engineering Journal, Vol. 11, pp. 541-553, 2006.
[12]Huang S.J., Lin C.Y., Chiu N.H., Fuzzy Decision Tree Approach for Embedding Risk Assessment Information into Software Cost Estimation Model, Journal of Information Science and Engineering, Vol. 22, Num. 2, pp. 297313, 2006.
[13]Somya Goyal, Anubha Parashar," Selecting the COTS Components Using Ad-hoc Approach ", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.7, No.5, pp. 22-31, 2017.DOI: 10.5815/ijwmt.2017.05.03
[14]Abbas S.A., et. al. “Cost Estimation-A Survey of Well-known Historic Cost Estimation Techniques”, Journal of Emerging Trends in Computing and Information Sciences, Vol. 3, No. 2, pp. 612-636, 2012.
[15]B.Boehm, C. Abts, S.Chulani, Software Development Cost Estimation ApproachesA Survey ,University of Southern California Centre for Software Engineering, Technical Report, USC-CSE-2000-505, 2000.
[16]L.H. Putnam, A general empirical solution to the macro software sizing and estimating problem, IEEE transactions on Software Engineering, 1978, Vol. 2, pp. 345- 361.
[17]A.C. Hodgkinson, and P.W. Garratt, “A neuro fuzzy cost estimator)”, Proceedings of Third International Conference on Software Engineering and Applications, 1999, pp. 401-406.
[18]M. Shepper and C. Schofield, Estimating software project effort using analogies, IEEE Tran. Software Engineering, vol. 23, pp. 736743, 1997.
[19]Burgess C.J. and Lefley M., “Can genetic programming improve software effort estimation? A comparative evaluation”, Information and Software Technology, 2001, Vol. 43, No. 14, pp. 863 -873.
[20]Eberhart, R. C., and Dobbins, R.W., “Neural Netwok PC Tools-A Practical Guide”, Academic Press Inc., San Diego CA, 1990.
[21]Maren, A., Hurston, C., and Pap, R., “Handbook of Neural Computing Applications”, Academic Press Inc., San Diego, CA, 1990.
[22]Simon Haykin, “Neural Networks-A Comprehensive Foundation”, Second Edition, Prentice Hall, 1998.
[23]N. K. Bose and P. Liang, “Neural Network Fundamentals with Graphs, Algorithms and Applications”, Tata McGraw Hill Edition,1998.
[24]B. Yegnanarayana, “Artificial Neural Networks”, Prentice Hall of India, 2003.
[25]Zahedi F., “An Introduction to Neural Networks and a Comparison with Artificial Intelligence and Expert Systems”, INTERFACES, 21:2 (March-April 1991), pp. 25-38.
[26]Y. MayaZaki and K. Mori, “COCOMO Evaluation and tailoring,” in Proceeding of the 8th International Conference on Software Engineering of the IEEE, 1985, pp.292-299.
[27]C.F. Kemerer, “An empirical validation of software cost estimation models”, Communication of the ACM, vol.30, no.5, 1987, pp.416-429.
[28]R. Chandrasekaran and R. V. Kumar, “On the Estimation of the Software Effort and Schedule using Constructive Cost Model-II and Function Point Analysis,” International Journal of Computer Applications, vol.44, no.9, 2012, pp3844.
[29]Tharwon Arnuphaptrairong, “A Literature Survey on the Accuracy of Software Effort Estimation Models”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2016 Vol II, IMECS 2016, March 16 - 18, 2016, Hong Kong.
[30]K.K. Aggarwal, Yogesh Singh, Pravin Chandra and Manimala Puri, “Evaluation of various training algorithms in a neural network model for software engineering applications”, ACM SIGSOFT Software Engineering , July 2005, Volume 3Number 4 , page1-4.
[31]Mrinal Kanti Ghose, Roheet Bhatnagar and Vandana Bhattacharjee, “Comparing Some Neural Network Models for Software Development Effort Prediction”, IEEE, 2011.
[32]Brooks, F.P., “The Mythical Man-Month”, Addison-Wesley, Reading Mass, 1975.
[33]Conte. S., Dunsmore, H. and Shen, V. , “Software Engineering Metrics and Models”, Benjamin/Cummings, Menlo Park. Calif., 1986.
[34]R. Jeffery, M. Ruhe and I. Wieczorek, “Using Public Domain Metrics to Estimate Software Development Effort”, Proceedings, Seventh International Software Metrics Symposium, 2001. METRICS 2001, p.16-27.
[35]S.D. Conte, H.E. Dunsmore, V.Y. Shen, “Software Engineering Metrics and Models”, The Benjamin/Cummings Publishing Company, Inc., 1986.