An NLP-Based Architecture for the Autocompletion of Partial Domain Models





Publicado en

Actas de las XXVI Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2022)




Domain models capture the key concepts and relationships of a business domain. Typically, domain models are manually defined by software designers in the initial phases of a software development cycle, based on their interactions with the client and their own domain expertise. Given the key role of domain models in the quality of the final system, it is important that they properly reflect the reality of the business. To facilitate the definition of domain models and improve their quality, we propose to move towards a more assisted domain modeling building process where an NLP-based assistant will provide autocomplete suggestions for the partial model under construction based on the automatic analysis of the textual information available for the project (contextual knowledge) and/or its related business domain (general knowledge). The process will also take into account the feedback collected from the designer's interaction with the assistant. We have developed a proof-of-concept tool and have performed a preliminary evaluation that shows promising results.


Acerca de Burgueño, Lola

Palabras clave

Assistant, Autocomplete, Domain Model, Modeling Recommendations, Natural Language Processing
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