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Applying model-driven engineering to the domain of chatbots: The Xatkit experience.

Authors :
Daniel, Gwendal
Cabot, Jordi
Source :
Science of Computer Programming. Jan2024, Vol. 232, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

Chatbots are becoming a common component of many types of software systems. But they are typically developed as a side feature using ad-hoc tools and custom integrations. Moreover, current frameworks are efficient only when designing simple chatbot applications while they still require advanced technical knowledge to define complex interactions and are difficult to evolve along with the company needs. In addition, the deployment of a chatbot application usually requires a deep understanding of the targeted platforms, especially back-end connections, increasing the development and maintenance costs. In this paper, we discuss our experiences building, evolving and distributing the Xatkit framework. Xatkit is a model-based framework built around a Domain-Specific Language to define chatbots (and voicebots and bots in general) in a platform-independent way. Xatkit also comes with a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic over the platforms of choice. Xatkit has significantly evolved since its initial release. This paper focuses on describing the evolution and the reasons (technical and non-technical) that triggered them. We believe our lessons learned can be useful to any other initiative trying to build a successful industrial-level chatbot platform, and in general, any type of model-based solution. • Model-driven engineering can be successfully applied to new domains such as AI-based software. • Model-driven tools have limitations when it comes to developing industrial-strength solutions. • Commercial success of model-driven based approaches depends on numerous factors beyond technical ones. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01676423
Volume :
232
Database :
Academic Search Index
Journal :
Science of Computer Programming
Publication Type :
Academic Journal
Accession number :
173724481
Full Text :
https://doi.org/10.1016/j.scico.2023.103032