Model-Driven Chatbot Development

Esta versión del artículo ha sido aceptada para su publicación, después de la revisión por pares (cuando corresponda) y está sujeta a los términos de uso de AM de Springer Nature, pero no es la Versión de Registro y no refleja mejoras posteriores a la aceptación, ni ninguna corrección. La versión del registro está disponible en línea en: https://doi.org/10.1007/978-3-030-62522-1_15Chatbots are software services accessed via conversation in natural language. They are increasingly used to help in all kinds of procedures like booking flights, querying visa information or assigning tasks to developers. They can be embedded in webs and social networks, and be used from mobile devices without installing dedicated apps. While many frameworks and platforms have emerged for their development, identifying the most appropriate one for building a particular chatbot requires a high investment of time. Moreover, some of them are closed – resulting in customer lock-in – or require deep technical knowledge. To tackle these issues, we propose a model-driven engineering approach to chatbot development. It comprises a neutral meta-model and a domainspecific language (DSL) for chatbot description; code generators and parsers for several chatbot platforms; and a platform recommender. Our approach supports forward and reverse engineering, and model-based analysis. We demonstrate its feasibility presenting a prototype tool and an evaluation based on migrating third party Dialogflow bots to RasaWork funded by the Spanish Ministry of Science (RTI2018095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314

Usability guidelines and evaluation criteria for conversational user interfaces: a heuristic and linguistic approach

Even though conversational user interfaces (CUI) have been studied since the 1950s, it is not yet fully understood what makes them feel natural, intuitive and usable. As a result, their design and evaluation poses major challenges. In this paper, we discuss how CUIs are different from other forms of human computer interaction, and what challenges and opportunities arise from these differences. We provide an overview of relevant linguistic principles for a natural language conversation and look at established high-level usability heuristics to derive a set of 53 technology-agnostic checkpoints specifically for text-based CUIs (a.k.a chatbots). These checkpoints have been evaluated with 15 professionals and academics from the fields of User Experience, Natural Language Processing, Conversation Analysis and linguistics to examine content validity. The resulting list of checkpoints provides both guidelines for the design and criteria for the evaluation of chatbots