Function point structure and applicability validation using the ISBSG dataset

Abstract

Background: The complexity of providing accurate software size estimation and effort prediction models is well known in the software industry, turning it into one of the most important research issues in empirical software engineering. Function points (FPA) is currently one of the most accepted software functional size metrics in the industry, but it is hardly automatable and generally requires a lengthy and costly process. Although accurate size estimation and effort prediction are very important for the success of any project, many practitioners have experienced difficulties in applying them. Objectives: This paper reports on a replicated study carried out on a subset of the ISBSG dataset to evaluate the structure and applicability of function points. The goal of this replication was to aggregate evidence and confirm results reported about internal issues of FPA as a metric using a different set of data. First, we examined FPA counting in order to determine which base functional components (BFC) were independent of each other and thus appropriate for an additive model of size. Second, we investigated the relationship between size and effort. Methods: A subset of the ISBSG dataset was used with 14 business application projects developed in C# from 2008 to 2011. We studied BFC independence and correlation between size, effort and productivity. FPA base functional components independence was checked with the Pearson and Kendall's Tau correlation coefficient. Besides, we studied the correlation between size and effort. Results: The replication aggregated evidence and confirmed that some BFC of the FPA method are correlated. There is a relationship between BFC unadjusted function points and effort. Limitations: This is an initial experiment of a research in progress that was performed on a small subset of 14 recent projects taken from the ISBSG dataset. Conclusions: Simplifying and automating a FPA measurement process based on counting BFC could encourage the adoption of FSM methods. Further research is needed.