Matching Antipatterns to Improve the Quality of Use Case Models

Use case modeling is an effective technique used to capture functional requirements. Use case models are mainly composed of textual descriptions written in natural language and simple diagrams that adhere to a few syntactic rules. This simplicity can be deceptive as many modelers create use case models that are incorrect, inconsistent, and ambiguous and contain restrictive design decisions. In this paper, a new methodology is described that utilizes antipatterns to detect potentially defective areas in use case models. This paper introduces the tool ARBIUM, which will support the proposed technique and aid analysts to improve the quality of their models. ARBIUM presents a framework that will allow developers to define their own antipatterns using OCL and textual descriptions. The proposed approach and tool are applied to a distributed biodiversity database use case model to demonstrate its feasibility. Our results indicate that they can improve the overall clarity and precision of use case models