Nikolaos Malamas, Konstantinos Panayiotou, Andreas L. Symeonidis

An increasing number of models and frameworks for Virtual Assistant (VA) development exist nowadays, following the progress in the Natural Language Processing (NLP) and Natural Language Understanding (NLU) fields. Regardless of their performance, popularity, and ease of use, these frameworks require at least basic expertise in NLP and software engineering, even for simple and repetitive processes, limiting their use only to the domain and programming experts. However, since the current state of practice of VA development is a straightforward process, Model-Driven Engineering approaches can be utilized to achieve automation and rapid development in a more convenient manner. To this end, we present \textit{dFlow}, a textual Domain-Specific Language (DSL) that offers a simplified, reusable, and framework-agnostic language for creating task-specific VAs in a low-code manner. We describe a system-agnostic VA meta-model, the developed grammar, and all essential processes for developing and deploying smart VAs. For further convenience, we create a cloud-native architecture and expose it through the Discord platform. We conducted a large-scale empirical evaluation with more than 200 junior software developers and collected positive feedback, indicating that dFlow can accelerate the entire VA development process, while also enabling citizen and software developers with minimum experience to participate.