Increasing the Semantic Similarity of Object-Oriented Domain Models by Performing Behavioral Analysis First

Abstract

The main goal of any object-oriented analysis (OOA) method is to produce a model that aids in understanding and communicating knowledge about a modeled domain. A higher degree of similarity among independently produced domain models provides an indication of how well the domain was understood by the different analysts, i. e. , more similar models indicate a closer and a more common understanding of a domain. A common understanding is of critical importance for effective knowledge communication and sharing. <br /><br /> The core of any OOA method is discovering and understanding concepts and their relationships in a domain. The main artifact produced by an OOA method is a domain model of the domain. A domain model often serves as the main source of design concepts during objectoriented design (OOD). This thesis evaluates two OOA methods by comparing the degree of similarity of the resulting domain models. <br /><br /> In particular, this work compares the semantic similarity of domain models extracted from use cases by <ol> <li>specification of sequence diagrams and then domain models, and </li> <li>specification of unified use case statecharts and then domain models. </li> </ol> The thesis makes case studies out of the application of the first method to 31 instances of large Voice-over-IP (VoIP) system and its information management system (IMS) and to 3 small elevator systems, and out of the application of the second method to 46 instances of the same large VoIP system and its IMS and to 12 instances of a medium-sized elevator system. <br /><br /> From an analysis of data from these case studies, the thesis concludes that there is an increase of 10% in the semantic similarity of domain models produced using the second method, but at the cost of less than or equal to 25% more analysis time.