Simulation and Comparison of Albrecht's Function Point and DeMarco's Function Bang Metrics in a CASE Environment.

Software size estimates provide a basis for soft- ware cost estimation during software development. Hence, it is important to measure the system size reliably as early as possible, i.e., during the requirements specification. Two best known specification level metrics are Albrecht's Function Points and DeMarco's Function Bang. One problem in using these metrics has been that there are only few tools that can calculate them during the specification phase. We have built one such tool. Another problem has been that no research data is available how these metrics correlate with one another. The paper compares these two metrics by a simulation study in which automatically generated randomized dataflow diagrams (DED's) were used as a statistical sample to count automatically function points and function bang in a built CASE environment. These value counts were correlated statistically using correlation coefficients and regression analysis. The simulation study permits sufficient variation in the base material to cover most types of system specifications. Moreover, it allows sufficient sampling sizes to make statistical analysis of data. The obtained results show that in certain cases there is a relatively good statistical correlation between these metrics. No overall general correlation exists, however. The paper does not show which one of the two metrics fares better as a size metric. Yet, our study suggests to use in many cases Function Bang metric, because its automatic calculation is simpler and depends less on judgement. Moreover, the study demonstrates that correlations depend upon a system type. This implies that in software projects one must be careful with size estimates while using these metrics. In order to know when one needs to calibrate the size estimate we need to develop algorithms which help to detect logical system types and make adjustments accordingly. The results also point out the need of empirical research in which we can better derive the connection between specification level metrics and the number of lines of code. [ABSTRACT FROM AUTHOR]