doi:10.1016/S0950-5849(98)00074-3 
Copyright © 1998 Elsevier Science B.V. All rights reserved
Construction of an FPA-type metric for early lifecycle estimation
Gerard Horgana, *, Souheil Khaddajb and Peter Forteb
a Nexis Associates Ltd., London, UK
b Computer Science and Electronic Systems, Kingston University, London, UK
Received 12 May 1997;
revised 6 February 1998;
accepted 6 April 1998.
Available online 4 March 1999.
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Abstract
The traditional function point approach has been well documented. Despite increasing popularity, investigations have shown a number of weaknesses. There is evidence that simple metrics may be as good as function points for early lifecycle estimates. This paper considers the use of a simplified approach to system size estimation that utilises function point elements. From this, the construction of a new model for producing function point estimates earlier in the development lifecycle is presented, together with results from application of the model to real project data.
Author Keywords: Function points; Size estimation; Project lifecycle
Index Terms: Productivity; Parameter estimation; Functions; Life cycle; Information theory; Function point analysis; Lifecycle estimation
Table 1. Example RFP and FPA dataset values
Table 2. The first dataset
Table 3. Observation sets used for the first dataset in estimating the FPA values
Table 4. FPA estimation accuracies for the first dataset
Table 5. Best case and worst case FPA estimation accuracies
Table 6. The second dataset
Table 7. Observation sets used for the second dataset in estimating the FPA values
Table 8. FPA estimation accuracies for the second dataset
Table 9. Best case and worst case FPA estimation accuracies
Table 10. Observation sets used for the second dataset in estimating the UFP values
Table 11. UFP estimation accuracies for the second dataset
Table 12. Best case and worst case UFP estimation accuracies
Table 13. FPA estimation accuracies for both datasets, using just the sum of the inputs and outputs
Table 14. UFP estimation accuracies for the second dataset, using just the sum of the weighted inputs and outputs
Table 15. UFP percentage contribution of each weighted function type, for the second dataset
Table 16. UFP and FPA estimation accuracies for the second dataset, using just the sum of the weighted outputs and weighted external logical files
Table 17. FPA estimation accuracies for both datasets, using regression equations
Table 18. Actual effort estimation accuracies for both datasets
Abstract
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