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Model Driven Engineering (MDE) adoption in the industry suffers from many technical and non-technical problems. One of the significant technical problems lies in the difficulty of building complex transformations from the composition of small and reusable transformations. Another problem resides in developing transformations from scratch in case they are missing. In this paper, we present an approach to how to handle these issues. The approach allows composing reusable transformations to build more complex ones by providing a catalog of prebuilt transformations targeting common architectures, frameworks, and design patterns. To give guidance and simplify the task of developing new transformations, we describe a platform description model of an entire system or a part of it in two views: a UML profile and a set of transformations. We also present three transformation types, each of which handles different abstraction design concerns. Generic transformations are small and reusable to build complex transformations, system-independent transformations are reusable and implement high-level design decisions, and system-specific transformations are not reusable and implement all design decisions needed for a given system. The approach is implemented as a plugin for a UML modeling tool and validated by developing a system that simulates the behavior of a gas station through model transformations built from the composition of reusable transformations.

Recommender Systems (RSs) have become essential for suggesting personalized recommendations to users across various fields, especially in tourism. Due to the rising popularity of mobile devices, mobile RSs have emerged as a potential research area. However, developing these systems into smart tourism mobile applications requires a significant investment in artificial intelligence experts and software engineering. Hence, to reduce the cost of this investment, we propose building a software factory that provides a robust set of assets such as domain-specific languages for accelerating the development process. To this aim, we apply a model-driven engineering approach that uses models, metamodels, and model transformations to support the designing and implementation of these software systems. In this paper, we introduce an overview of our software factory to support the development of several models of RSs.

Conversational agents are being increasingly adopted in various domains, such as e-commerce and customer services, and as a direct communication channel between companies and end-users. Several tools have been developed to facilitate their definition and deployment. They exploit existing cloud infrastructures and artificial intelligence (AI) techniques to efficiently process users’ input and extract conversational information. Major Information Technology (IT) companies, such as Google, IBM, Microsoft, and Amazon, have provided powerful tools to develop conversational agents. Still, choosing the most appropriate tool is not easy, as it may require high costs associated with automatic natural language processing (NLP) services and expertise in software engineering and AI. Therefore, this paper aims to analyze different tools to help developers and non-developers to choose the optimal tool for their specific scenario of creating a conversational agent.

Recent advances in artificial intelligence and natural language processing have been widely used in recent years, and one of the best applications of these technologies is conversational agents. These agents are computer programs that can converse with users in natural languages. Developing conversational agents using artificial intelligence techniques is an exciting prospect in natural language processing. In this study, we built an intelligent conversational agent using deep learning techniques. We used a sequence-to-sequence model with encoder–decoder architecture. This encoder–decoder uses a recurrent neural network with long–short-term memory cells. The encoder was used to understand the user’s question, and the decoder was to provide the answer.

Several approaches have been proposed to accelerate software development. Model-driven engineering (MDE) is one approach that aims to abstract and automate the development process using models and transformations as the main artifacts. This paper presents our model-driven approach to developing smart tourism mobile applications.