Abstract Conversational user interfaces (CUIs), such as chatbots, are becoming a common component of many software systems. Although they are evolving in many directions (such as advanced language processing features, thanks to new AI-based developments), less attention has been paid to access control and other security concerns associated with CUIs, which may pose a clear risk to the systems they interface with. In this paper, we apply model-driven techniques to model and enforce access-control policies in CUIs. In particular, we present a fully fledged framework to integrate the role-based access-control (RBAC) protocol into CUIs by: (1) modeling a set of access-control rules to specify permissions over the bot resources using a domain-specific language that tailors core RBAC concepts to the CUI domain; and (2) describing a mechanism to show the feasibility of automatically generating the infrastructure to evaluate and enforce the modeled access control policies at runtime.

Abstract Software systems start to include other types of interfaces beyond the “traditional” Graphical-User Interfaces (GUIs). In particular, Conversational User Interfaces (CUIs) such as chat and voice are becoming more and more popular. These new types of interfaces embed smart natural language processing components to understand user requests and respond to them. To provide an integrated user experience all the user interfaces in the system should be aware of each other and be able to collaborate. This is what is known as a multiexperience User Interface. Despite their many benefits, multiexperience UIs are challenging to build. So far CUIs are created as standalone components using a platform-dependent set of libraries and technologies. This raises significant integration, evolution and maintenance issues. This paper explores the application of model-driven techniques to the development of software applications embedding a multiexperience User Interface. We will discuss how raising the abstraction level at which these interfaces are defined enables a faster development and a better deployment and integration of each interface with the rest of the software system and the other interfaces with whom it may need to collaborate. In particular, we propose a new Domain Specific Language (DSL) for specifying several types of CUIs and show how this DSL can be part of an integrated modeling environment able to describe the interactions between the modeled CUIs and the other models of the system (including the models of the GUI). We will use the standard Interaction Flow Modeling Language (IFML) as an example “host” language.

The specification of a family of software products may include documents written in natural language. Automatically extracting knowledge from these documents is a challenging problem that requires using Natural Language Processing (NLP) techniques. This knowledge can be formalized as a Feature Model (FM), a diagram capturing the key features and the relationships among them.

In order to make improvements to teaching, it is vital to know what students think of the way they are taught. With that purpose in mind, exhaustively analyzing the forums associated with the subjects taught at the Universitat Oberta de Cataluya (UOC) would be extremely helpful, as the university's students often post comments on their learning experiences in them. Exploiting the content of such forums is not a simple undertaking. The volume of data involved is very large, and performing the task manually would require a great deal of effort from lecturers. As a first step to solve this problem, we propose a tool to automatically analyze the posts in forums of communities of UOC students and teachers, with a view to systematically mining the opinions they contain. This article defines the architecture of such tool and explains how lexical-semantic and language technology resources can be used to that end. For pilot testing purposes, the tool has been used to identify students' opinions on the UOC's Business Intelligence master's degree course during the last two years. The paper discusses the results of such test. The contribution of this paper is twofold. Firstly, it demonstrates the feasibility of using natural language parsing techniques to help teachers to make decisions. Secondly, it introduces a simple tool that can be refined and adapted to a virtual environment for the purpose in question.

Domain Specific Visual Languages (DSVLs) play a cornerstone role in Model-Driven Engineering (MDE), where (domain specific) models are used to automate the production of the final application. Graph Transformation is a formal, visual, rule-based technique, which is increasingly used in MDE to express in-place model transformations like refactorings, animations and simulations. However, there is currently a lack of methods able to perform static analysis of rules, taking into account the DSVL meta-model integrity constraints. In this paper we propose a lightweight, efficient technique that performs static analysis of the weak executability of rules. The method determines if there is some scenario in which the rule can be safely applied, without breaking the meta-model constraints. If no such scenario exists, the method returns meaningful feedback that helps repairing the detected inconsistencies.