An Empirical Investigation to Understand the Difficulties and Challenges of Software Modellers When Using Modelling Tools

Software modelling is a challenging and error-prone task. Existing Model-Driven Engineering (MDE) tools provide modellers with little aid, partly because tool providers have not investigated users' difficulties through empirical investigations such as field studies. This paper presents the results of a two-phase user study to identify the most prominent difficulties that users might face when developing UML Class and State-Machine diagrams using UML modelling tools. In the first phase, we identified the preliminary modelling challenges by analysing 30 Class and State-Machine models that were previously developed by students as a course assignment. The result of the first phase helped us design the second phase of our user study where we empirically investigated different aspects of using modelling tools: the tools' effectiveness, users' efficiency, users' satisfaction, the gap between users' expectation and experience, and users' cognitive difficulties. Our results suggest that users' greatest difficulties are in (1) remembering contextual information and (2) identifying and fixing errors and inconsistencies.NSERC CREATE, 465463-2015 || NSERC Discovery Grant, 155243-1

Improving Learning Outcomes in UML Sequence Diagrams Through Reduced Cognitive Load

This paper demonstrates how cognitive load theory can be used to improve learning outcomes by presenting a tool capable of assisting novices to learn to model sequence diagrams effectively. Sequence diagrams are known to lead to heavy cognitive load as they must be consistent with the class diagram, while discharging all the responsibilities specified in the underlying use case. Moreover, novices must also consider the various design options and their impact on the qualitative aspects of the model. Our tool allows cognitive load to be better managed by using a ‘divide and conquer’ approach. In the initial stage students need to focus only on consistency aspects, and they will not be allowed violate the constraints stated in the class diagram. In the second stage, students will not be allowed to submit a diagram until the stated use case goals are met. In the final stage qualitative feedback and marks are awarded based on established metrics and students are allowed to improve their scores by resubmitting the model. Qualitative and quantitative results show that our novel tool using a form of gamification has helped to improve the learning outcomes in modelling substantially, especially for the stragglers. One benefit of our approach is that it can be adapted to other areas where students maybe cognitively challenged

UCAnDoModels: A Context-based Model Editor for Editing and Debugging UML Class and State-Machine Diagrams

© ACM 2019 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected] face cognitive challenges when using model editors to edit and debug UML models, which make them reluctant to adopt modelling. To assist practitioners in their modelling tasks, we have developed effective and easy-to-use tooling techniques and interfaces that address some of these challenges. The principle philosophy behind our tool is to employ cognitive-based techniques such as Focus+Context interfaces and increased automation of modelling tasks, in order to provide the users with valid, relevant and meaningful contextual information that are essential to fulfil a focus task (e.g., writing a transition expression). This paper presents our approach, which we call User-Centric and Artefact-Centric Development of Models (UCAnDoModels), and discusses two usecase scenarios to demonstrate how our tooling techniques can enhance the user experience with modelling tools.NSERC CREATE 465463-2015 NSERC Discovery Grant 155243-1

A Focus+Context Approach to Alleviate Cognitive Challenges of Editing and Debugging UML Models

Copyright (c) 2019 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Model-Driven Engineering has been proposed to increase the productivity of developing a software system. Despite its benefits, it has not been fully adopted in the software industry. Research has shown that modelling tools are amongst the top barriers for the adoption of MDE by industry. Recently, researchers have conducted empirical studies to identify the most severe cognitive difficulties of modellers when using UML model editors. Their analyses show that users’ prominent challenges are in remembering the contextual information when performing a particular modelling task; and locating, understanding, and fixing errors in the models. To alleviate these difficulties, we propose two Focus+Context user interfaces that provide enhanced cognitive support and automation in the user’s interaction with a model editor. Moreover, we conducted two empirical studies to assess the effectiveness of our interfaces on human users. Our results reveal that our interfaces help users 1) improve their ability to successfully fulfil their tasks, 2) avoid unnecessary switches among diagrams, 3) produce more error-free models, 4) remember contextual information, and 5) reduce time on tasks.NSERC CREATE 465463-2015 NSERC Discovery Grant 15524

System: A core conceptual modeling construct for capturing complexity

[EN] The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggest-ing the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of "system"should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research.We wish to thank the editor-in-chief, Carson Woo, and three anonymous reviewers for their exceptionally insightful and developmental comments. The substantial improvements that resulted from their feedback were much deeper than we usually experience in journal review processes. We wish to thank the participants of www.nlnature.com (now inactive) who contributed their sightings from 2010 to 2022. We also thank Jeffrey Parsons and Yolanda Wiersma - the co -investigators of NLNature. We are grateful to the late Mario Bunge and to Ron Weber with whom we discussed ontological ideas that inspired this paper. We also want to thank the participants and reviewers of AIS SIGSAND and ER Conference for their comments and feedback on earlier versions of this paper. This research was supported by McIntire School of Commerce, University of Virginia, J. Mack Robinson College of Business, Georgia State University, United States, and by VRAIN Research Institute of the Universitat Politecnica de Valencia and the Generalitat Valenciana, Spain under the CoMoDiD project (CIPROM/2021/023) .Lukyanenko, R.; Storey, VC.; Pastor López, O. (2022). System: A core conceptual modeling construct for capturing complexity. Data & Knowledge Engineering. 141:1-29. https://doi.org/10.1016/j.datak.2022.10206212914

A User-Centric Approach to Improve the Quality of UML-like Modelling Tools and Reduce the Efforts of Modelling

As software systems grow in size and complexity, their development and maintenance are becoming increasingly challenging. Model-Driven Engineering (MDE) has been proposed as a means to increase the developer's productivity of such large-scale complex software systems. Despite its benefits, MDE has not been fully adopted in the software industry due to several barriers. Research has shown that modelling tools are amongst the top barriers for the industry's reluctance to adopt MDE, mostly because there is a little investigation of the modellers' interactions with modelling tools when editing and debugging models, which are cognitively difficult tasks. More specifically, MDE tool research has not considered 1) a thorough analysis of modellers and their tasks, to understand their challenges of using modelling tools, 2) the underlying human-cognitive factors, and 3) a systematic assessment of the effectiveness of proposed solutions (i.e., tooling techniques) on human users. This thesis argues that MDE tools can be enhanced to overcome (some of) the challenges of adoption by considering human-cognitive factors (i.e., user-centric) when designing and proposing model-easing techniques for model editors. We advance our thesis in three main steps. As a first step, we conducted an empirical study to identify the most-severe cognitive difficulties of modellers when using UML model editors. In our study, we asked the recruited subjects to perform several model-editing and model-debugging tasks. We collected information during the sessions that could help us understand the subjects' cognitive challenges. The results show that users face multiple challenges, amongst which the most prominent challenges are remembering contextual information when performing a particular modelling task; and locating, understanding, and fixing errors in the models. In the second step, we identified the cognitive factors that drive the most prominent challenges and subsequently devised several tooling advancements that provide enhanced cognitive support and automation in the users' interaction with a model editor. The philosophy behind our tooling advancements is to provide the contextual information that are relevant to performing a particular modelling task, thereby, alleviating the modellers' cognitive challenges of recollecting information from different diagrams. We also proposed an on-the-fly error-resolution technique that aims at resolving errors as they occur. We implemented our Eclipse-based model-editor and embedded our tooling techniques in the tool. Lastly, we conducted two empirical studies to assess the effectiveness of our model-editor on human users. The Context study aimed at evaluating our tool's ability to reduce the challenges of remembering contextual information, whereas the Debugging study aimed at assessing our tool's ability to improve the users' experience of debugging models. Our results reveal that our interfaces help users 1) improve their ability to successfully fulfil their tasks, 2) avoid unnecessary context switches among diagrams, 3) produce more error-free models, 4) remember contextual information, and 5) reduce time on tasks