Background: Functional size measures of software are widely used for effort estimation, because they provide a fairly objective quantification of software size, which is one of the main factors affecting software development effort. Unfortunately, in some conditions, performing the standard Function Point Analysis process may be too long and expensive. Moreover, functional measures could be needed before functional requirements have been elicited completely and at the required detail level. Aim: Basing effort estimation on measures that are simpler than Function Points—hence, faster and cheaper to obtain—could be beneficial, if good estimation accuracy is possible. In this paper, the level of accuracy that can be obtained using simple measures instead of Function Points—as defined by IFPUG, the International Function Point User Group—is evaluated, based on an empirical study. Method: An analysis of the data provided in the ISBSG dataset was performed. Effort models based on standard IFPUG Function Points and on the number of transactions were derived. The accuracy of the effort estimates delivered by the two models were then compared. Results: Effort models based on the number of transactions appear marginally less accurate than models based on standard IFPUG Function Points for new development projects, and marginally more accurate for projects extending previously developed software. Conclusions: Based on the results of the empirical study, it seems that using the number of transactions instead of the standard IFPUG Function Points measures does not cause estimate accuracy to change to a practically appreciable extent. This conclusion applies to effort models using size measure as the only independent variable. Additional research is necessary to evaluate the performance of effort models based on the number of transactions in combination with other factors.

Using Extremely Simplified Functional Size Measures for Effort Estimation

Lavazza, Luigi
;
Liu, Geng;
2020-01-01

Abstract

Background: Functional size measures of software are widely used for effort estimation, because they provide a fairly objective quantification of software size, which is one of the main factors affecting software development effort. Unfortunately, in some conditions, performing the standard Function Point Analysis process may be too long and expensive. Moreover, functional measures could be needed before functional requirements have been elicited completely and at the required detail level. Aim: Basing effort estimation on measures that are simpler than Function Points—hence, faster and cheaper to obtain—could be beneficial, if good estimation accuracy is possible. In this paper, the level of accuracy that can be obtained using simple measures instead of Function Points—as defined by IFPUG, the International Function Point User Group—is evaluated, based on an empirical study. Method: An analysis of the data provided in the ISBSG dataset was performed. Effort models based on standard IFPUG Function Points and on the number of transactions were derived. The accuracy of the effort estimates delivered by the two models were then compared. Results: Effort models based on the number of transactions appear marginally less accurate than models based on standard IFPUG Function Points for new development projects, and marginally more accurate for projects extending previously developed software. Conclusions: Based on the results of the empirical study, it seems that using the number of transactions instead of the standard IFPUG Function Points measures does not cause estimate accuracy to change to a practically appreciable extent. This conclusion applies to effort models using size measure as the only independent variable. Additional research is necessary to evaluate the performance of effort models based on the number of transactions in combination with other factors.
2020
Proceedings of the 14th ACM/IEEEInternational Symposium on EmpiricalSoftware Engineering and Measurement
9781450375801
14th ACM/IEEEInternational Symposium on EmpiricalSoftware Engineering and Measurement
on-line
October 5-7, 2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2100175
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