Software development effort estimation using function points and simpler functional measures: a comparison

Background-Functional Size Measures are widely used for estimating the development effort of software. After the introduction of Function Points, a few "simplified"measures have been proposed, aiming to make measurement simpler and quicker, but also to make measures applicable when fully detailed software specifications are not yet available. It has been shown that, in general, software size measures expressed in Function Points do not support more accurate effort estimation with respect to simplified measures. Objective-Many practitioners believe that when considering "complex"projects, i.e., project that involve many complex transactions and data, traditional Function Points measures support more accurate estimates than simpler functional size measures that do not account for greater-Then-Average complexity. In this paper, we aim to produce evidence that confirms or disproves such belief. Method-Based on a dataset that contains both effort and size data, an empirical study is performed, to provide some evidence concerning the relations that link functional size (measured in different ways) and development effort. Results-Our analysis shows that there is no statistically significant evidence that Function Points are generally better at estimating more complex projects than simpler measures. Function Points appeared better in some specific conditions, but in those conditions they also performed worse than simpler measures when dealing with less complex projects. Conclusions-Traditional Function Points do not seem to effectively account for software complexity. To improve effort estimation, researchers should probably dedicate their effort to devise a way of measuring software complexity that can be used in effort models together with (traditional or simplified) functional size measures