The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.