The most popular Functional Size Measurement methods adopt a concept of “functionality” that is based mainly on the data involved in functions and data movements. Functional size measures are often used as a basis for estimating the effort required for software development. However, Functional Size Measurement does not take directly into consideration the amount of data processing involved in a process, even though it is well-known that development effort does depend on the amount of data processing code to be written. Thus, it is interesting to investigate to what extent the most popular functional size measures represent the data processing features of requirements and, consequently, the amount of data processing code to be written. To this end, we consider three applications that provide similar functionality, but require different amounts of data processing. These applications are then measured via a few Functional Size Measurement methods and traditional size measures (such as Lines of Code). A comparison of the obtained measures shows that differences among the applications are best represented by differences in Lines of Code. It is likely that the actual size of an application that requires substantial amounts of data processing is not fully represented by functional size measures. In summary, the paper shows that not taking into account data processing dramatically limits the expressiveness of the functional size measures. Practitioners that use size measures for effort estimation should complement functional size measures with measures that quantify data processing, to obtain precise effort estimates.
A Study on the Difficulty of Accounting for Data Elaboration in Functional Size Measures
LAVAZZA, LUIGI ANTONIO;MORASCA, SANDRO;TOSI, DAVIDE
2015-01-01
Abstract
The most popular Functional Size Measurement methods adopt a concept of “functionality” that is based mainly on the data involved in functions and data movements. Functional size measures are often used as a basis for estimating the effort required for software development. However, Functional Size Measurement does not take directly into consideration the amount of data processing involved in a process, even though it is well-known that development effort does depend on the amount of data processing code to be written. Thus, it is interesting to investigate to what extent the most popular functional size measures represent the data processing features of requirements and, consequently, the amount of data processing code to be written. To this end, we consider three applications that provide similar functionality, but require different amounts of data processing. These applications are then measured via a few Functional Size Measurement methods and traditional size measures (such as Lines of Code). A comparison of the obtained measures shows that differences among the applications are best represented by differences in Lines of Code. It is likely that the actual size of an application that requires substantial amounts of data processing is not fully represented by functional size measures. In summary, the paper shows that not taking into account data processing dramatically limits the expressiveness of the functional size measures. Practitioners that use size measures for effort estimation should complement functional size measures with measures that quantify data processing, to obtain precise effort estimates.
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