Abstract

Software effort estimation models has been an area of considerable research for many years and it is still a challenge for software engineering. Although Functional Size Measurement (FSM) methods have become widely used, effort estimation based on the functional size still needs further research. Unbiased and comprehensive comparison between prediction models is needed. Some studies suggest that the relationship between effort and the base functional components of a FSM method would improve estimation models. This paper evaluates the structure of COSMIC FFP base functional components and its applicability in functional size based effort estimation models. Our study reports a benchmarking experiment evaluating 600 learning schemes for 12 ISBSG R12 sub datasets in business application projects which were sized by the COSMIC FSM method. In total, 7,200 runs were conducted (Learning schemes X Datasets) and the best learning schemes were reported by dataset. Lessons learned after conducting the experiment are discussed.

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