A Meta-model for Documenting Conversational Requirements in Chatbots

Context: Chatbots emerged in the decade of 60 and are currently a popular type of application that provides the experience of talking to a human-like robot. However, the similarity of their behaviour to a human is also what makes the elicitation and documentation of conversational-related requirements a hard task, due to the subjective nature of conversations. Currently there are no standards or guidelines to register and test subjective requirements, present in human conversations. Methods: We proposed a metamodel for documentation of conversational requirements and conducted the validation through a 2-step questionnaire. We then analysed the feedback received and applied improvements in the model instances. Results: Based on metamodel we propose the conversational integrated map, an artifact that correlates the subjective requirements with their associated KPIs. Validation was conducted through a simulation with 4 practitioners in a 2-step questionnaire. Artifact format choice revealed a preference for mainly textual and table models. Scalability and necessary time to complete were points of divergence between answers. Conclusion: Proposed artifact was well accepted and could provide pre-made models available for edition for ease of use. Future steps of this study include the elaboration of a guide of best practices on chatbot documentation.