Insights on How to Enhance the Detection of Modeling Errors by iStar Novice Learners
Hajer Mejri · American Scientific Research Journal for Engineering, Technology, and Sciences (Global Society of Scientific Research and Researchers) · 2017
When teaching a new paradigm which involves practical training to a large group of students, it often becomes time-consuming and impractical for a single instructor to give advice on an individual basis on how to correct errors being made and the need for computer-aided assistants arises. In this work, we focus on the i* (iStar) framework which is being used to teach requirements engineering and modeling techniques to undergraduate and graduate students in the classroom. We proposed and developed an online tool for automating the work of checking the design constructs used in i* diagrams so that novice learners could detect and correct errors on their own without the assistance of a human expert nearby. Although the tool was useful in showing novice learners how to edit their models to make them free of syntax errors, there were a number of situations in which they could not recognize the semantical design flaws and defects of a model using the feedback from the tool. In this paper, we give examples of the errors we observed and recommend a new tool which will automatically generate a human understandable textual annotation of the main model elements and the relationships connecting them to assist beginners as well as non-technical stakeholders involved in the requirement decisions of a system with detecting simple misrepresentations of information of this type that need to be rectified in the model.