Metrics for Database Systems: An Empirical Study

An important task for any software project manager is to be able to predict and control project size and development effort. Unfortunately, there is comparatively little work, other than function points, that tackles the problem of building prediction systems for software that is dominated by data considerations, in particular systems developed using 4GLs. We describe an empirical investigation of 70 such systems. Various easily obtainable counts were extracted from data models (e.g. number of entities) and from specifications (e.g. number of screens). Using simple regression analysis, a prediction system of implementation size with accuracy of MMRE = 21% was constructed. This approach offers several advantages. First, there tend to be fewer counting problems than with function points since the metrics we used were based upon simple counts. Second, the prediction systems were calibrated to specific local environments rather than being based upon industry weights. We believe this enhanced their accuracy. Our work shows that it is possible to develop simple and useful local prediction systems based upon metrics easily derived from functional specifications and data models, without recourse to overly complex metrics or analysis techniques. We conclude that this type of use of metrics can provide valuable support for the management and control of 4GL and database projects.