An NLP-based architecture for the autocompletion of partial domain models (original) (raw)

Communication Dans Un Congrès Année : 2021

Shuai Li

Résumé

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.

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https://hal.science/hal-03010872

Soumis le : mardi 17 novembre 2020-20:38:52

Dernière modification le : mardi 3 septembre 2024-11:16:05

Archivage à long terme le : jeudi 18 février 2021-20:30:50

Dates et versions

hal-03010872 , version 1 (17-11-2020)

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Loli Burgueño, Robert Clarisó, Shuai Li, Sébastien Gérard, Jordi Cabot. An NLP-based architecture for the autocompletion of partial domain models. 33rd International Conference on Advanced Information Systems Engineering (CAiSE'21), Jun 2021, Melbourne, Australia. ⟨hal-03010872⟩

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