We set down the principles behind a modeling language for quantum software. We present a minimal set of extensions to the wellknown Unified Modeling Language (UML) that allows it to effectively model quantum software. These extensions are separate and independent of UML as a whole. As such they can be used to extend any other software modeling language, or as a basis for a completely new language. We argue that these extensions are both necessary and sufficient to model, abstractly, any piece of quantum software. Finally, we provide a small set of examples that showcase the effectiveness of the extension set.

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Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design‐level requirements analysis. The success of the software system is mostly dependent on how well users’ requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human–computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design‐level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers’ decision making when designing user interfaces.

Our goal is to enable rapid production of static and dynamic object models from natural language description of problems. Rapid modeling is achieved through automation of analysis tasks. This automation captures the cognitive schemes analysts use to build their models of the world through the use of a precise methodology. The methodology is based on the use of proposed technique called role posets, and a semi-natural language (called 4W).Original problem statements are automatically translated to 4W language. The produced sentences then, are analyzed with role posets to produce static model views. Finally the 4W sentences are used to generate dynamic views of the problem. This set of methods maximizes analysis process agility, promotes reusability and constitutes a valuable tool in the learning process of object thinking.