Traditional project effort estimation utilizes development models that span the entire project life cycle and thus culminates estimation errors. This exploratory research is the first attempt to break each project activity down to smaller work elements. There are eight work elements, each of which is being defined and symbolized with visually distinct shape. The purpose is to standardize the operations associated with the development process in the form of a visual symbolic flow map. Hence, developers can streamline their work systematically. Project effort estimation can be determined based on these standard work elements that not only help identify essential cost drivers for estimation, but also reduce latency cost to enhance estimation efficiency. Benefits of the proposed work element scheme are project visibility, better control for immediate pay-off and, in long term management, standardization for software process automation.
KeywordsProject Effort EstimationWork ElementSymbolic Flow MapLatency CostSoftware Process Automation
Putnam, L. (1978) A General Empirical Solution to the Macro Software Sizing and Estimating Problem. IEEE Transactions on Software Engineering, SE-4, 345-361. http://dx.doi.org/10.1109/TSE.1978.231521
Boehm, B. (1981) Software Engineering Economics. Prentice Hall PTR, Upper Saddle River.
Boehm, B., Abts, C., Brown, A., Chulani, S., Clark, B., Horowitz, E., Madachy, R., Reifer, D. and Steece, B. (2000) Software Cost Estimation with COCOMO II. Prentice Hall PTR, Upper Saddle River.
Walston, C.E. and Felix, C.P. (1977) A Method of Programming Measurement and Estimation. IBM Systems Journal, 16, 54-73. http://dx.doi.org/10.1147/sj.161.0054
Bailey, J.W. and Basili, V.R. (1981) A Meta-Model for Software Development Resource Expenditures. Proceedings of the 5th International Conference on Software Engineering, 107-116.
Albrecht, A.J. and Gaffney Jr., J.E. (1983) Software Function, Source Lines of Code, and Development Effort Prediction. IEEE Transactions on Software Engineering, SE-9, 639-648. http://dx.doi.org/10.1109/TSE.1983.235271
Kemerer, C.F. (1987) An Empirical Validation of Software Cost Estimation Models. IEEE Transactions on Software Engineering, 30, 416-429.
Matson, J.E., Barrett, B.E. and Mellichamp, J.M. (1994) Software Development Cost Estimation Using Function Points. IEEE Transactions on Software Engineering, 20, 275-287. http://dx.doi.org/10.1109/32.277575
Albrecht, A. (1979) Measuring Application Development Productivity. I. B. M. Press, New York, 83-92.
Karner, G. (1993) Resource Estimation for Objectory Projects. Objective Systems SF AB.
Shepperd, M. and Schofield, C. (1997) Estimating Software Project Effort Using Analogies. IEEE Transactions on Software Engineering, 23, 736-743. http://dx.doi.org/10.1109/32.637387
Li, J.Z., Ruhe, G., Al-Emran, A. and Richter, M.M. (2007) A Flexible Method for Software Effort Estimation by Analogy. Empirical Software Engineering, 12, 65-106. http://dx.doi.org/10.1007/s10664-006-7552-4
Nasir, M. (2006) A Survey of Software Estimation Techniques and Project Planning Practices. Proceedings of the seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD’06), Las Vegas, 19-20 June 2006, 305-310.
Yang, Y., He, M., Li, M., Wang, Q. and Boehm, B. (2008) Phase Distribution of Software Development Effort. Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM’08), Kaiserslautern, 9-10 October 2008, 61-69.
Jodpimai, P., Sophatsathit, P. and Lursinsap, C. (2010) Estimating Software Effort with Minimum Features using Neural Functional Approximation. Proceedings 2010 International Conference on Computational Science and Its Applications (ICCSA), Fukuoka, 23-26 March 2010, 266-273. http://dx.doi.org/10.1109/ICCSA.2010.63
Dejaeger, K., Verbeke, W., Martens, D. and Baesens, B. (2012) Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Transactions on Software Engineering, 38, 375-397. http://dx.doi.org/10.1109/TSE.2011.55
Taylor, F.W. (1911) The Principles of Scientific Management. Harper & Brothers, New York and London, LCCN11010339, OCLC233134.
Gilbreth, F. and Gilbreth, C.E. (1948) Cheaper by the Dozen.
Kocaguneli, E., Menzies, T. and Keung, J.W. (2012) On the Value of Ensemble Effort Estimation. IEEE Transactions on Software Engineering, 38, 1403-1416. http://dx.doi.org/10.1109/TSE.2011.111
Gencel, C. and Demirors, O. (2008) Functional Size Measurement Revisited. ACM Transactions on Software Engineering and Methodology, 17, 15:1-15:36.
Jones, C. (1995) Backfiring: Converting Lines of Code to Function Points. Computer, 28, 87-88. http://dx.doi.org/10.1109/2.471193
Tsunoda, M., Kakimoto, T., Monden, A. and Matsumoto, K.-I. (2011) An Empirical Evaluation of Outlier Deletion Methods for Analogy-Based Cost Estimation. Proceedings of the 7th International Conference on Predictive Models in Software Engineering, Banff, 20-21 September 2011, 17:1-17:10.
Keller, J.M., Gray, M.R. and Givens, J.A. (1985) A Fuzzy K-nearest Neighbor Algorithm. IEEE Transactions on Systems, Man and Cybernetics, SMC-15, 580-585. http://dx.doi.org/10.1109/TSMC.1985.6313426
Kitchenham, B., Pickard, L., MacDonell, S. and Shepperd, M. (2001) What Accuracy Statistics Really Measure [Software Estimation]. Proceedings IEE—Software, 148, 81-85.
Foss, T., Stensrud, E., Kitchenham, B. and Myrtveit, I. (2003) A Simulation Study of the Model Evaluation Criterion MMRE. IEEE Transactions on Software Engineering, 29, 985-995. http://dx.doi.org/10.1109/TSE.2003.1245300
Friedman, M. (1937) The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. American Statistical Association, 32, 675-701. http://dx.doi.org/10.1080/01621459.1937.10503522
Abran, A. and Robillard, P. (1996) Function Points Analysis: An Empirical Study of Its Measurement Processes. IEEE Transactions on Software Engineering, 22, 895-910. http://dx.doi.org/10.1109/32.553638
Redei, G.P. (2008) Encyclopedia of Genetics, Genomics, Proteomics and Informatics. Springer, Heidelberg.
de Vries, D. and Chandon, Y. (2007) On the False-Positive Rate of Statistical Equipment Comparisons Based on the Kruskal-Wallis HStatistic. IEEE Transactions on Semiconductor Manufacturing, 20, 286-292. http://dx.doi.org/10.1109/TSM.2007.901398
Brooks, F.P. (1987) No Silver Bullet. Computer, 20, 10-19. http://dx.doi.org/10.1109/MC.1987.1663532