Model-based early and rapid estimation of COSMIC functional size – An experimental evaluation

AbstractContext onal size measurement methods are widely used but have two major shortcomings: they require a complete and detailed knowledge of user requirements, and they involve relatively expensive and lengthy processes. ive routinely used in the software industry to effectively describe software requirements in an incremental way, so UML models grow in detail and completeness through the requirements analysis phase. Here, we aim at defining the characteristics of increasingly more refined UML requirements models that support increasingly more sophisticated – hence presumably more accurate – size estimation processes. sider the COSMIC method and three alternative processes (two of which are proposed in this paper) to estimate COSMIC size measures that can be applied to UML diagrams at progressive stages of the requirements definition phase. Then, we check the accuracy of the estimates by comparing the results obtained on a set of projects to the functional size values obtained with the standard COSMIC method. s alysis shows that it is possible to write increasingly more detailed and complete UML models of user requirements that provide the data required by COSMIC size estimation methods, which in turn yield increasingly more accurate size measure estimates of the modeled software. Initial estimates are based on simple models and are obtained quickly and with little effort. The estimates increase their accuracy as models grow in completeness and detail, i.e., as the requirements definition phase progresses. sion pers that use UML for requirements modeling can obtain an early estimation of the application size at the beginning of the development process, when only a very simple UML model has been built for the application, and can obtain increasingly more accurate size estimates while the knowledge of the product increases and UML models are refined accordingly.