A study on the statistical convertibility of IFPUG Function Point, COSMIC Function Point and Simple Function Point

Abstract Background Several functional size measurement methods have been proposed. A few ones –like IFPUG and COSMIC methods– are widely used, while others –like Simple Function Points method– are interesting new proposals, which promise to deliver functional size measures via a faster and cheaper measurement process. Objectives Since all functional size measurement methods address the measurement of the same property of software (namely, the size of functional specifications), it is expected that measures provided in a given measurement unit can be converted into a different measurement unit. In this paper, convertibility of IFPUG Function Points, COSMIC Function Points, and Simple Function Points is studied. Method Convertibility is analyzed statistically via regression techniques. Seven datasets, each one containing measures of a set of software applications expressed in IFPUG Function Points, COSMIC Function Points and Simple Function Points, were analyzed. The components of functional size measures (usually known as Base Functional Components) were also involved in the analysis. Results All the analyzed measures appear well correlated to each other. Statistically significant quantitative models were found for all the combinations of measures, for all the analyzed datasets. Several models involving Base Functional Components were found as well. Conclusions From a practical point of view, the paper shows that converting measures from a given functional size unit into another one is viable. The magnitude of the conversion errors is reported, so that practitioners can evaluate if the expected conversion error is acceptable for their specific purposes. From a conceptual point of view, the paper shows that Base Functional Components of a given method can be used to estimate measures expressed in a different measurement unit: this seems to imply that different functional size measurement methods are ‘structurally’ strongly correlated.

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