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(to be completed)

 


ThinkMind // International Journal On Advances in Software, volume 6, numbers 1 and 2, 2013 // View article soft_v6_n12_2013_1


An Empirical Evaluation of Simplified Function Point Measurement Processes

Authors:
Luigi Lavazza
Geng Liu

Keywords: Functional Size Measures; Function Points; Simplified measurement processes; Early&Quick Function Points (EQFP); NESMA estimated; NESMA indicative

Abstract:
Function Point Analysis is widely used, especially to quantify the size of applications in the early stages of development, when effort estimates are needed. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified methods have been proposed to measure Function Points. We used simplified methods for sizing both “traditional” and Real-Time applications, with the aim of evaluating the accuracy of the sizing with respect to full-fledged Function Point Analysis. To this end, a set of projects, which had already been measured by means of Function Point Analysis, have been measured using a few simplified processes, including those proposed by NESMA, the Early&Quick Function Points, the ISBSG average weights, and others; the resulting size measures were then compared. We also derived simplified size models by analyzing the dataset used for experimentations. In general, all the methods that provide predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to size tend to be quite inaccurate. In general, different methods show different accuracy for Real-Time and non Real-Time applications. The results of the analysis reported here show that in general it is possible to size software via simplified measurement processes with an acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. Deriving our own models from the project datasets proved possible, and yielded results that are similar to those obtained via the methods proposed in the literature.

Pages: 1 to 13

Copyright: Copyright (c) to authors, 2013. Used with permission.

Publication date: June 30, 2013

Published in: journal

ISSN: 1942-2628

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