Development of Software Tool for Scheduling Risk Analysis
Keywords:
Scheduling, Risk, Analysis, Project, Approach, Distribution, Tool, Development.Abstract
There is a need in software industry for a tool to integrate scheduling risk. This tool will answer a multitude of problems in achieving a higher degree of scheduling accuracy. Successful project requires effective specialized tool for reducing risk. The objective of this study is to develop a project scheduling tool for the estimation of project risk. This study provides empirical evidence that scheduling tool are very much relevant, valid, and applicable in software industry. The output of this study is a tool which employs some of the main distribution methods such as Uniform, Beta, Triangular and Gaussian in order to decrease the risk of project delays. This tool was tested and the results were extracted, analyzed and discussed. The tool was developed using JAVA. It can be recommended that using a different type of probability distributions on project’s activities will give the project schedule more accurate estimations by using the developed tool early in the project development lifecycle.
References
Q. W. Fleming and K. M. Joel, "Earned Value Project Management A Powerful Tool for Software Projects," Software Management, vol. 16, p. 337, 2006.
Z. Jia, F. Zhou and L. Yong, "Research on Risk Manage of Power Construction Project Based on Bayesian Network," in Intelligent Computing and Information Science, Berlin, 2011.
I. Attarzadeh and S. H. Ow , "Improving Estimation Accuracy of the COCOMO II Using an Adaptive Fuzzy Logic Model," in IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, 2011.
M. Vanhoucke, "On the dynamic use of project performance and schedule risk information during projecttracking," Omega , vol. 39, no. 4, pp. 416-426, 2011.
E. Zio, "The future of risk assessment," Reliability Engineering & System Safety, vol. 177, pp. 176-190, 2018.
G. F. Dubos, S. H. Joseph and B. Robert, "Technology readiness level, schedule risk, and slippage in spacecraft design," Journal of Spacecraft and Rockets, vol. 45, no. 4, pp. 836-842, 2008.
J. Wang and L. Yung-I, "An overlapping process model to assess schedule risk for new product development," Computers & Industrial Engineering, vol. 57, no. 2, pp. 460-474, 2007.
A. Terje, "The risk concept—historical and recent development trends," Reliability Engineering & System Safety, vol. 99, pp. 33-44, 2012.
L. Tao, D. Wu, S. Liu and J. H. Lambert, "Schedule risk analysis for new-product development: The GERT method extended by a characteristic function," Reliability Engineering & System Safety, vol. 167, pp. 464-473, 2017.
M. A. Fischer and A. Florian, "Scheduling with computer-interpretable construction method models," Journal of Construction Engineering and Management, vol. 122, no. 4, pp. 337-347, 1996.
Y. Ben-Haim and L. Alexander, "Robust reliability of projects with activity-duration uncertainty," Journal of construction engineering and management, vol. 124, no. 2, pp. 125 - 132, 1998.
W.-C. Wang and A. D. Laura, "Model for evaluating networks under correlated uncertainty—NETCOR," Journal of Construction Engineering and Management, vol. 126, no. 6, pp. 458-466, 2000.
R. Margea and M. Camelia, "Open source approach to project management tools," Informatica Economica, vol. 15, no. 1, p. 196, 2011.
Standish Group International, Inc, "CHAOS Summary report," Standish Group International, Inc, 2009.
L. Wallace, M. Keil and A. Rai, "How Software Project Risk Affects Project Performance: An Investigation of the Dimensions of Risk and an Exploratory Model," Decision Sciences, vol. 35, no. 2, pp. 289-321, 2004.
P. Kulik and K. Weber, Software Risk Management Practices, Dayton: KLCI Research Group, 2001.
T. Kendrick, Identifying and managing project risk: essential tools for failure-proofing your project, Amacom, 2015.
Project management Institute, A Guide to the Project Management Body of Knowledge ( PMBOK® Guide), 4 ed., Projet Management Institute, 2008, pp. 69-90.
J. W. Chinneck, Practical optimization: a gentle introduction, Ottawa, Ottawa: Carleton University, 2015.
W. Herroelen, "Project scheduling—Theory and practice," Production and operations management, vol. 14, no. 4, pp. 413-432, 2005.
W. G. Sullivan, E. M. Wicks and J. Luxhoj, Engineering Economy, 13 ed., Pearson Prentice Hall: Pearson Prentice Hall, 2006, pp. 510-539..
J. Hollmann, "The monte-carlo challenge: A better approach," AACE International Transactions, pp. RI31-RI37, 2007.
D. G. Goldstein and R. David, "Lay understanding of probability distributions," Judgment & Decision Making, vol. 9, no. 1, 2014.
D. Johnson, "The Triangular Distribution as a Proxy for the Beta Distribution in Risk Analysis," Journal of the Royal Statistical Society. Series D (The Statistician), vol. 46, no. 3, pp. 387-398, 1997.
R. Y. Rubinstein and D. P. Kroese, Simulation and the Monte Carlo Method, 2 ed., New York: Wiley & Sons, 2007.
S. M. Ross, A First Course in Probability, 7 ed., Englewood Cliffs, NJ: Prentice-Hall , 2005.
B. J. Oates, Researching information systems and computing, Sage, 2005.
A. Drozdek, Data Structures and algorithms in C++, Cengage Learning, 2012.
X. Xu, J. Wang, C. Z. Li, W. Huang and N. Xia, "Schedule risk analysis of infrastructure projects: A hybrid dynamic approach," Automation in Construction, vol. 95, pp. 20-34, 2018.
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