Establishing of Function Point Process Based On Stochastic Distribution
This study aims to establish function point process
based on stochastic distribution. In order to demonstrate effectiveness
of the study we present a case study that it applies suggested method
on an automotive electrical and electronics system software
development based on Monte Carlo Simulation. It is expected that the
result of this paper is used as guidance for establishing function point
process in organizations and tools for helping project managers make
decisions correctly.
[1] U. S. Rao, K. Srikanth and P. Chinmay, “Stochastic Optimization
Modeling and Quantitative Project Management”, IEEE Software, 2008.
[2] M. K. Khedr, “Project risk management using Monte Carlo simulation,”
AACE International Transactions, 2006.
[3] B. Ergashev, “Estimating the lognormal-gamma model of operational risk
using the Markov chain Monte Carlo method,” Available at
SSRN1316428, 2009.
[4] J. Dasgupta, G. Sahoo and R. P. Mohanty, “Monte carlo simulation based
estimations: Case from a global outsourcing company,” in Technology
Management Conference (ITMC), IEEE International, IEEE, 2011.
[5] CMMI Product Team, “CMMI for Development Version 1.3,” Software
Engineering Institute of Carnegie Mellon, 2010.
[6] C. Y. Kim and H. S. Han, “Applying Monte Carlo Simulation for
Software Project Risk Management Method,” MA. dissertation,
University of SangMyung, Seoul, Korea, 2011.
[7] P. Musilek, W. Pedrycz, N. Sun and G. Succi, “On the sensitivity of the
COCOMO II Software Cost Estimation model,” in Proceedings of the
eighth symposium on Software Metrics, IEEE Computer Society, 2002.
[8] S. G. Park and J. Y. Park, “A Study for Software Sizing Method,” The
Korea Computer Industry Education Society, vol. 2, pp 471-480, 2004.
[9] J. S. Srivastava and G. Singh, “Optimized GSCs in Function Point
Analysis - A Modified Approach,” International Journal of Research and
Reviews in Applied Sciences, vol. 17, 2013.
[10] H. K. Raju, Y. T. Krishnegowda, “Software Sizing and Productivity with
Function Points,” Lecture Notes on Software Engineering, vol. 1, 2013.
[1] U. S. Rao, K. Srikanth and P. Chinmay, “Stochastic Optimization
Modeling and Quantitative Project Management”, IEEE Software, 2008.
[2] M. K. Khedr, “Project risk management using Monte Carlo simulation,”
AACE International Transactions, 2006.
[3] B. Ergashev, “Estimating the lognormal-gamma model of operational risk
using the Markov chain Monte Carlo method,” Available at
SSRN1316428, 2009.
[4] J. Dasgupta, G. Sahoo and R. P. Mohanty, “Monte carlo simulation based
estimations: Case from a global outsourcing company,” in Technology
Management Conference (ITMC), IEEE International, IEEE, 2011.
[5] CMMI Product Team, “CMMI for Development Version 1.3,” Software
Engineering Institute of Carnegie Mellon, 2010.
[6] C. Y. Kim and H. S. Han, “Applying Monte Carlo Simulation for
Software Project Risk Management Method,” MA. dissertation,
University of SangMyung, Seoul, Korea, 2011.
[7] P. Musilek, W. Pedrycz, N. Sun and G. Succi, “On the sensitivity of the
COCOMO II Software Cost Estimation model,” in Proceedings of the
eighth symposium on Software Metrics, IEEE Computer Society, 2002.
[8] S. G. Park and J. Y. Park, “A Study for Software Sizing Method,” The
Korea Computer Industry Education Society, vol. 2, pp 471-480, 2004.
[9] J. S. Srivastava and G. Singh, “Optimized GSCs in Function Point
Analysis - A Modified Approach,” International Journal of Research and
Reviews in Applied Sciences, vol. 17, 2013.
[10] H. K. Raju, Y. T. Krishnegowda, “Software Sizing and Productivity with
Function Points,” Lecture Notes on Software Engineering, vol. 1, 2013.
@article{"International Journal of Information, Control and Computer Sciences:71633", author = "Do Syung Ryong and Kang Hyun Su", title = "Establishing of Function Point Process Based On Stochastic Distribution", abstract = "This study aims to establish function point process
based on stochastic distribution. In order to demonstrate effectiveness
of the study we present a case study that it applies suggested method
on an automotive electrical and electronics system software
development based on Monte Carlo Simulation. It is expected that the
result of this paper is used as guidance for establishing function point
process in organizations and tools for helping project managers make
decisions correctly.", keywords = "Function Point, Monte Carlo Simulation, Software
Estimation, Stochastic Distribution.", volume = "9", number = "4", pages = "1037-7", }