Computing based effort estimation in software development of global project

Computing based effort estimation in software development of global project

  IJCOT-book-cover
 		 International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2017 by IJCOT Journal
Volume - 7 Issue - 1
Year of Publication : 2017
Authors :   Kiran Ahsan
DOI : 10.14445/22492593/IJCOT-V40P301

Citation

Kiran Ahsan "Computing based effort estimation in software development of global project ", International Journal of Computer & organization Trends (IJCOT), V7(1):9-17 Jan - Feb 2017, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract

The aim is to analysis of software development effort. It needs hold up of a well defined analysis plan to rank each conjecture technique. This paper is bases on the analysis of the estimation models that are helpful make to the project managers for the estimation. Estimation software program development has always recently been characterized by certain variables. In this case global software is developed this is certainly one among important challenges for application software developers that predicting the expansion effort of software on with the distribution upon the basis of particulars, size, complexity, time, cost and additional different measures level. The standard research topic relates to the effort of web based application development with the size base technique which is covered the use case and function point analysis. The method is concerning with the size of software project which is enhanced the existing model of effort estimation with the analysis of function points. A proposed model is improved the performance of the cost estimation in allocated given away and combined software tasks. This paper is helps to estimate the effort of the early stages of distributed software to improve the correctness and to avoid the dependency of the hassle and value estimation. That is removed the repeat work in the developing and help to low the cost of the project from the saving of errors and repeated work of the project.

References

1. Abdukalykov, R., Hussain, I., Kassab, M., & Ormandjieva, O. (2011, August). Quantifying the accuracy of software development effort estimation using projects clustering. IETsoftware, 6(6), 461-473.
2. Aljahdali, S., & Sheta, A. F. (2010, May). Software effort estimation by tuning COOCMO model and locality: Insight on improving software effort estimation. Information and Software Technology.
3. Alweshah, M., W. Ahmed and H. Aldabbas.2015. Evolution of Software Reliability Growth Models. A Comparison of Auto-Regression and Genetic Programming Models. Evolution, 125 (3)20-25
4. Aleem, S., L.F. Capretz, and F. Ahmed.2015. Benchmarking Machine Learning Technologies for Software Defect Detection, 6 (3): 11-23
5. Aggarwal, G., and V.K Gupta.2014. Software reliability growth model. International Journal of Advanced Research in Computer Science and Software Engineering, 4 (1):475-479.
6. Amin, A., L. Grunske and A. Colman.2013. An approach to software reliability prediction based on time series modeling. Journal of Systems and Software, 86 (7): 1923-1932.
7. ALRahamneh, Z., M. Reyalat, A.F. Sheta, S.B. Ahmad and S.A Oqeili.2011. A New Software Reliability Growth Model.Genetic-Programming-Based Approach. Journal of Softwar Engineering and Applications, 4 (8): 476.
8. Afzal, W., and R. Torkar. 2008. Suitability of genetic programming for software reliability growth modeling. In International Symposium on Computer Science and its Applications,2(1) 114-117.
9. 8.Ando, T., H. Okamura and T. Dohi. 2006. Estimating Markov modulated software reliability models via EM algorithm. In Dependable, Autonomic and Secure Computing, 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing, 1(1): 111- 118.
10. Aljahdali, S.H., A. Sheta and D. Rine. 2001. Prediction of software reliability: A comparison between regression and neural network non-parametric models. In Computer Systems and Applications, ACS/IEEE International Conference,1(5): 470-473.
11. Beck, A., J. Trumper and J. Dollner, 2012. A Visual Analysis and Design Tool for Planning Software Re-engineerings. Journal of IEEE Computer Society, 23(5):92-96.
12. Bianchi, A., D. Ciavano and G. Visaggio, 2003. Iterative Reengineering of Legacy Systems. Journal of IEEE Computer Society, 29(3):225-226.
13. Baloian, N., J. A. Pino, C. Reveco and G. Zurita, 2013. Mobile Collaboration for Business Process Elicitation from an Agile Development Methodology Viewpoint. e-Business Engineering (ICEBE), 2013 IEEE 10th International Conference, IEEE Computer Society, 5(9): 306-311.
14. Bardsiri, V. K., Jawawi, D. N. A., Hashim, S. Z. M., & Khatibi, E. (2012). Increasing the Applications (SITA), 2015 10th International Conference on (pp. 1-6). IEEE. Braga, P. L., Oliveira, A. L., & Meira, S. R. (2007, October). Software effort estimation using Communication Systems and Networks (CICSyN), 2012 Fourth International Conference on(pp. 167-172). IEEE.
15. Batool, A., Y. Hafeez, S. Asghar, M. A. Abbas and M. S. Hassan, 2013. A Scrum Framework for Requirement Engineering Practices. Proceedings of the Pakistan Academy of Sciences, 50(4):263–270.
16. Batool, A., Y. H. Motla, B. Hamid, S. Asghar, M. Riaz, M. Mukhtar and M. Ahmed, 2013. Comparative Study of Traditional Requirement Engineering and Agile Requirement Engineering. Advanced Communication Technology (ICACT), 2013 15th International Conference, IEEE Computer Society, 5(9): 1006-1014.
17. Cagnin, M. I., J. C. Maldonado, F. S. R. Germano and R. D. Penteado, 2003. PARFAIT: Towards a Framework-based Agile Reengineering Process. Journal of IEEE Computer Society, 29(9):22-31.
18. Cagnin, M. I., J. C. Maldonado, F. S. R. Germano and R. D. Penteado, 2004. An Agile Reverse Engineering Process based on a Framework. Journal of IEEE Computer Society, 12(6):240-254.
19. Chawla, G. and S.K. Thakur. 2013. A Fault Analysis based Model for Software Reliability Estimation. International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878.
20. Costa, E.O., A.T.R. Pozo and S.R. Vergilio.2010. A genetic programming approach for software reliability modeling. Reliability, IEEE Transactions on, 59(1): 222-230.
21. Chiu, K.C., Y.S. Huang and T.Z. Lee.2008. A study of software reliability growth from the perspective of learning effects. Reliability Engineering & System Safety, 93 (10):1410- 1421.
22. Costa, E.O., G.A.D. Souza, A.T.R. Pozo and S.R. Vergilio.2007. Exploring genetic programming and boosting techniques to model software reliability. Reliability, IEEE Transactions on, 56 (3): 422-434.

Keywords
Conjecture, new formed. Variable, Adjustable. Predicting, Consequence of something.