The week's pick
Reseach Article
Performance Evaluation of Machine Learning Techniques using Software Cost Drivers
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 89 - Number 16 |
| Year of Publication: 2014 |
| Authors: Manas Gaur |
10.5120/15714-4525
|
Manas Gaur . Performance Evaluation of Machine Learning Techniques using Software Cost Drivers. International Journal of Computer Applications. 89, 16 ( March 2014), 10-18. DOI=10.5120/15714-4525
Abstract
There is a tremendous rise in cost of software, used in organizations. The cost of software ranges from hundred thousand to millions of dollars. The prediction of the software cost beforehand is the challenging area as the rough estimates and the actual cost varies with large differences. The traditional methods are being used since birth of software engineering. These methods based on current project needs, defines the cost based on appropriate weights assigned to scale factors and cost drivers. Application of artificial intelligence in software project planning has given a new methodology for Software Cost Estimation (SCE) that has improved, prediction accuracy. This methodology named Machine Learning Techniques (MLTs) lays emphasis on, similarity to past projects and correlation in the data (training data). Our research work has considered 10 projects along with their costs based on the cost drivers. Using Machine Learning Techniques (MLTs), the research tries to predict the cost, based on the cost drivers. The performance of MLTs was analyzed using root means square error and squared error.
References
- Venkatachalam, A. R 1993 Software Cost Estimation Using Artificial Neural Networks, Proceedings of International Joint Conference on Neural Network, pp. 987-990.
- Samson, Bill 1997 Software cost estimation using an Albus perceptron (CMAC), Information and Software technology ELSEVIER, pp. 55-60,1997.
- Boehm, B. W, 1981 Software Engineering Economics, Software Engineering Books, Prentice Hall.
- Krishnamurthy, S. and Fisher, D. 1995 Machine Learning Approaches to Estimating Software Development Effort, IEEE transaction on Software Engineering, pp. 126-137.
- Malhotra, Ruchika, Kaur, Arvinder and Singh, Yogesh 2010 Machine Learning Methods for Software Effort Prediction, ACM SIGSOFT Software Engineering Notes, pp. 1-6.
- Attarzadeh, Iman and Hock Ow, Siew, 2011 Improving Estimation Accuracy of the COCOMO II Using an Adaptive Fuzzy Logic Model, IEEE International Conference on Fuzzy Systems, pp. 2458-2464.
- Agarwal, K. K and Singh, Yogesh, 2005 Software Engineering, Software Engineering Books, New Age International Publishers.
- Rish, I, 2001 An empirical study of naïve Bayes classifier, IJCAI workshop on empirical methods in artificial intelligence, pp. 41-46.
- Kabir, Md Faisal, 2011 Enhanced Classification Accuracy on Naïve Bayes Data Mining Models, International Journal of Computer Application, pp. 9-16.
- Idri, Ali and Elyassami, Sanaa , 2011 Applying Fuzzy ID3 Decision Tree for Software Effort Estimation, International Journal of Computer Science Issues, pp. 131-138.
- Baskeles, Bilge, 2007 Software Effort Estimation Using Machine Learning Methods, IEEE 22nd Symposium on Computer and Information Sciences, pp. 1-6.
- Papatheocharous, Efi, 2012 Software Cost Modelling and Estimation Using Artificial Neural Network Enhanced Input Sensitivity Analysis, Journal of Universal Computer Science, pp. 1-30.
Index Terms
Keywords