ER-Based Software Sizing for Data-Intensive Systems

Abstract

Despite the existence of well-known software sizing methods such as Function Point method, many developers still continue to use ad-hoc methods or so called “expert” approaches. This is mainly due to the fact that the existing methods require much implementation information that is difficult to identify or estimate in the early stage of a software project. The accuracy of ad-hoc and “expert” methods also has much problem. The entity-relationship (ER) model is widely used in conceptual modeling (requirements analysis) for data-intensive systems. From our observation, the characteristic of a data-intensive system, and therefore the source code of its software, is well characterized by the ER diagram that models its data. Based on this observation, this paper proposes a method for building software size model from extended ER diagram through the use of regression models. We have collected some real data from the industry to do a preliminary validation of the proposed method. The result of the validation is very encouraging. As software sizing is an important key to software cost estimation and therefore vital to the industry for managing their software projects, we hope that the research and industry communities can further validate the proposed method.