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How Does UML Look and Sound? Using AI to Interpret UML Diagrams Through Multimodal Evidence
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Generative Artificial Intelligence (GAI) has emerged as a transformative force in higher education, offering both challenges and opportunities. This paper explores the multifaceted impact of GAI on academic work, with a focus on student life and, in particular, the implications for international students. While GAI, exemplified by models like ChatGPT, has the potential to revolutionize education, concerns about academic integrity have arisen, leading to debates on the use of AI detection tools. This essay highlights the difficulties in reliably detecting AI-generated content, raising concerns about potential false accusations against students. It also discusses biases within AI models, emphasizing the need for fairness and equity in AI-based assessments with a particular emphasis on the disproportionate impact of GAI on international students, who already face biases and discrimination. It also highlights the potential for AI to mitigate some of these challenges by providing language support and accessibility features. Finally, this essay acknowledges the disruptive potential of GAI in higher education and calls for a balanced approach that addresses both the challenges and opportunities it presents by emphasizing the importance of AI literacy and ethical considerations in adopting AI technologies to ensure equitable access and positive outcomes for all students. We offer a coda to Ng et al.’s AI competency framework, mapped to the Revised Bloom’s Taxonomy, through a lens of cultural competence with AI as a means of supporting educators to use these tools equitably in their teaching.
We propose a vision for inclusive conceptual modeling. The urgency to address inclusiveness comes from two converging trends: the deepening reliance on information technology; and broader engagement of the members of the public in IT development and use, including conceptual modeling. In this paper, we propose inclusive conceptual modeling as a critical direction for conceptual modeling research, explain its basic tenets and illustrate corresponding research opportunities.
In the last years, many methods and tools for generating Unified Modeling Language (UML) class diagrams from natural language (NL) software requirements. These methods and tools deal with the transformation of NL textual requirements to UML diagrams. The transformation process involves analyzing NL requirements and extracting relevant information from the text to generate UML class models. This paper aims to survey the existing works of transforming textual requirements into UML class models to indicate their strengths and limitations. The paper provides a comprehensive explanation and evaluation of the existing approaches and tools. The automation degree, efficiency, and completeness, as well as the used techniques, are studied and analyzed. The study demonstrated the necessity of automating the process, in addition to combining artificial intelligence with engineering requirements and using Natural Language Processing (NLP) techniques to extract class diagrams from NL requirements.
Understanding conceptual models of business domains is a key skill for practitioners tasked with systems analysis and design. Research in this field predominantly uses experiments with specific user proxy cohorts to examine factors that explain how well different types of conceptual models can be comprehended by model viewers. However, the results from these studies are difficult to compare. One key difficulty rests in the unsystematic and fluctuating consideration of model viewer characteristics (MVCs) to date. In this paper, we review MVCs used in prominent prior studies on conceptual model comprehension. We then design an empirical review of the influence of MVCS through a global, cross-sectional experimental study in which over 500 student and practitioner users were asked to answer comprehension questions about a prominent type of conceptual model - BPMN process models. As an experimental treatment, we used good versus bad layout in order to increase the variance of performance. Our results show MVC to be a multi-dimensional construct. Moreover, process model comprehension is related in different ways to different traits of the MVC construct. Based on these findings, we offer guidance for experimental designs in this area of research and provide implications for the study of MVCs.
We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design
using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice.
While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on collaboration. Being one of constraint-based
tutors, COLLECT-UML represents the domain knowledge as a set of constraints. However, it is the first system to also represent
a higher-level skill such as collaboration using the same formalism. We started by developing a single-user ITS that supported
students in learning UML class diagrams. The system was evaluated in a real classroom, and the results showed that students’
performance increased significantly. In this paper, we present our experiences in extending the system to provide support
for collaboration as well as domain-level support. We describe the architecture, interface and support for collaboration in
the new, multi-user system. The effectiveness of the system has been evaluated in two studies. In addition to improved problem-solving
skills, the participants both acquired declarative knowledge about effective collaboration and did collaborate more effectively.
The participants have enjoyed working with the system and found it a valuable asset to their learning.
This edition of the “Practitioner’s Digest” department focuses on an initiative for reviewing papers reporting empirical research, along with recent papers about empirical studies with implications for software practice.
The reports on Disability by the World Health Organization show that the number of people with disabilities is increasing. Consequently, accessibility should play an essential role in information systems engineering research. While software and web engineering research acknowledge this need by providing, e.g., web accessibility guidelines and testing frameworks, we show in this paper, based on a systematic review of the literature and current modeling tools, that accessibility is, so far, a blind spot in conceptual modeling research. With the paper at hand, we aim to identify current research gaps and delineate a vision toward more inclusive, i.e., disability-aware conceptual modeling. One key finding relates to a gap in research and tool support concerning physical disabilities. Based on these results, we further present the first modeling tool that can be used keyboard-only, thereby including users with physical disabilities to engage in conceptual modeling.
This paper describes MATA (Modeling Aspects Using a Transformation Approach), a UML aspect-oriented modeling (AOM) technique
that uses graph transformations to specify and compose aspects. Graph transformations provide a unified approach for aspect
modeling in that the methods presented here can be applied to any modeling language with a well-defined metamodel. This paper,
however, focuses on UML class diagrams, sequence diagrams and state diagrams. MATA takes a different approach to AOM since
there are no explicit joinpoints. Rather, any model element can be a joinpoint, and composition is a special case of model
transformation. The graph transformation execution engine, AGG, is used in MATA to execute model compositions, and critical
pair analysis is used to automatically detect structural interactions between different aspect models. MATA has been applied
to a number of realistic case studies and is supported by a tool built on top of IBM Rational Software Modeler.
Simple and controllable music generation
- J Copet
Zero-shot text-to-image generation (2021) Google Scholar
- A Ramesh
Multimodal process mining
- A Gavric
- D Bork
- H Proper