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{{Paper
|id=Vol-3685/short5
|storemode=property
|title=ModeLLer – Enabling End-Users to Model Systems: a Case Study in Digital Agriculture
|pdfUrl=https://ceur-ws.org/Vol-3685/short5.pdf
|volume=Vol-3685
|authors=Chiara Mannari,Elisa Anichini,Manlio Bacco,Alessio Ferrari,Tommaso Turchi,Alessio Malizia
|dblpUrl=https://dblp.org/rec/conf/avi/MannariAB0TM24
}}
==ModeLLer – Enabling End-Users to Model Systems: a Case Study in Digital Agriculture==
https://ceur-ws.org/Vol-3685/short5.pdf
ModeLLer – Enabling End-Users to Model Systems:
a Case Study in Digital Agriculture
Chiara Mannari1,2 , Elisa Anichini1 , Manlio Bacco2 , Alessio Ferrari2 , Tommaso Turchi1 and
Alessio Malizia1,3
Department of Computer Science, University of Pisa, Italy
Institute of Information Science and Technologies "Alessandro Faedo" - ISTI, CNR, Pisa, Italy
Faculty of Logistics, Molde University College, Norway
Abstract
Digital technologies show promising potential in the development of sustainable agriculture. For example,
the combination of cloud and edge paradigms, 5G, and the Internet of Things (IoT) allows the development
of sophisticated applications — e.g., for food traceability, pest detection, and automatic irrigation — with the
possibility to also exploit Artificial Intelligence (AI)-powered techniques. At the same time, digitalisation in
agriculture is a socio-technical process that involves several classes of stakeholders with diverse backgrounds and
skills, e.g., in farming or technology. Model-driven approaches leveraging diagrammatic notations can support
information exchange between different domains. In fact, the development of models can be a co-design practice
involving end-users throughout all phases of creation because of their expressive power. However, current
modelling platforms are typically oriented toward engineers, and there is a lack of tools accessible to end-users for
designing and modelling systems. In this position paper, we present ModeLLer, a prototype of a web environment
for modelling systems based on an intuitive visual language that can be exported into standard code. The aim is
to increasingly involve users in modelling their digital ecosystems as a task for developing digital applications.
Keywords
end-user development, process modelling, digital agriculture, living labs, meta-design, socio-technical systems
1. Introduction
Digital technologies have a great potential to affect the future of labour. Internet of Things (IoT)
components allow the development of sophisticated applications, different techniques based on Artificial
Intelligence (AI) contribute to automating manual tasks, and 5G networking will make technology
accessible and affordable to users. Tang et al. [1] show the promising potential of digital technologies
in the agricultural domain. Furthermore, many solutions are being developed, e.g., for food traceability,
pest detection, and smart irrigation aiming to align digitalisation pathways to the UN 2030 agenda of
Sustainable Development Goals (SDGs) [2]. Recent studies shift the paradigm from a technology-driven
to a sustainable one, carrying out interdisciplinary research focusing on the impacts of digitalisation [3]
and analysing drivers and barriers [4]. In such contexts, it is increasingly important to include users in
participatory activities of design, development and assessment of digital technologies. We carry out our
research in the framework of the European project CODECS — Maximizing the co-benefits of agricultural
digitalisation through conducive digital ecosystems - that aims at developing indicators for sustainable
digitalisation starting from the analysis of the transformation occurring to business processes after
the introduction of digital technology [5]. To fulfil such objectives in CODECS, an interdisciplinary
research group and multiple living labs (LLs), the latter as key research units in the field, have been set
up. LLs are networks of farmers, knowledge intermediaries, stakeholders, and policymakers addressing
emerging challenges by co-developing user-friendly approaches, methods, and tools in real contexts [6].
Proceedings of the 8th International Workshop on Cultures of Participation in the Digital Age (CoPDA 2024): Differentiating and
Deepening the Concept of "End User" in the Digital Age, June 2024, Arenzano, Italy
$ chiara.mannari@isti.cnr.it (C. Mannari); e.anichini1@studenti.unipi.it (E. Anichini); manlio.bacco@isti.cnr.it (M. Bacco);
alessio.ferrari@isti.cnr.it (A. Ferrari); tommaso.turchi@unipi.it (T. Turchi); alessio.malizia@unipi.it (A. Malizia)
https://chiaramannari.github.io/ (C. Mannari)
0000-0002-5488-4150 (C. Mannari); 0000-0001-6733-1873 (M. Bacco); 0000-0002-0636-5663 (A. Ferrari);
0000-0001-6826-9688 (T. Turchi); 0000-0002-2601-7009 (A. Malizia)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
The involvement of multiple stakeholders primarily implies the sharing of knowledge about domains,
systems, and processes, and several challenges arise related to information exchange between partic-
ipants with different backgrounds and expertise. To mitigate issues in communication, we propose
the adoption of model-driven requirements engineering techniques [7]. These approaches are used in
different stages of the software engineering process and can be particularly effective in exchanging
information with users and customers [8]. The most widespread procedure to develop the models is
based on the elicitation of knowledge through interviews to domain experts, followed by a phase in
which engineers formalise the models through diagrams. In this position paper, we aim to further extend
the modelling practice to end-users, i.e., users with no expertise in formal notation by enabling them
to generate formal representations of their system. Thus, we address the following research question:
How to make end-users autonomous in the representation of systems without knowing formal languages?
To answer the question, we developed ModeLLer, a prototype of a web tool based on a block-based
interface to support modelling by end-users. With ModeLLer, we aim to apply a meta-design approach
to LLs. At this stage, we focus on the development of UML class diagrams [9] that can be adopted to
represent systems, by providing an overview of the actors, resources, tools, as well as relationships.
2. Methodology
Meta-design and LLs represent the conceptual underpinning of this research. The former, as a framework
aimed at defining and creating socio-technical infrastructures in which new forms of collaborative
design and development can take place [10]. The latter, as user-centred innovation ecosystems where
participants actively collaborate to solve real problems and create new solutions, integrating research
and development processes in real contexts [6]. Agricultural LLs are a widespread method for identifying
problems and working on common solutions, and new tools are being developed to support participatory
activities. An example is provided by the Living Lab Modeler 1 . The tool offers a digital twin of a LL
facilitating the LL assessment, coordination, and dissemination.
As described in a previous work [11], in the framework of CODECS, our research team is working on
the development of a method for socio-technical process modelling based on a set of different diagrams,
namely UML, i* and BPMN to represent the process reengineering after the introduction of digital
technology. We plan to apply the method to 20 European LLs with the aim to provide easy-to-read
graphical representations of agricultural processes transformed by digital technologies that can be used
by different stakeholders to understand how digitalisation may reshape a context. Moreover, the models
can be adopted by economists and sociologists for further evaluations such as cost-benefit analysis.
Currently, the diagrams are developed through a collaborative procedure involving domain experts
and experts on the notations, the former providing the necessary information to create the models and
the latter proceeding to the formalisation through specialised software. In a previous paper [12], we
described the co-design activity performed in a LL to create a UML class diagram. In this study, we
aim to enable domain experts to autonomously create and edit the diagrams. We preliminary identified
a main challenge related to the use of specialised modelling software by end-users; in fact, there is a
trade-off between code-oriented software and generalist web platforms, the former is usually complete
in representing all language features but has limited user experience2 . The latter have powerful user
experience but usually lack the formal code component3 . Furthermore, the design freedom offered by
the blank canvas provided by modelling software can pose challenges to users who need to be guided in
the creation of structured representations. Thus, the motivation behind the development of ModeLLer
is to provide a tool meeting the needs of LLs to model systems and processes without any knowledge of
formal notations. To develop the tool, we adopt the SER method for socio-technical systems [13]; the
prototype and the walkthrough presented in the next section correspond to the first seeding phase and
future work will include the evolution of the prototype following users’ priorities.
1
https://www.livinglabmodeler.eu
2
See for example Eclipse Papyrus https://www.eclipse.org/papyrus/, accessed 29 March 2024
3
See for example Lucidspark https://lucidspark.com/, accessed 29 March 2024
Figure 1: Creation of precision irrigation model with ModeLLer
3. The ModeLLer Prototype
ModeLLer is a prototype of a web environment for modelling systems based on a block-based visual
language which can be exported into standard code4 .
Currently, the functionalities offered by the prototype allow the creation of UML class diagrams
through the interaction with a block-based visual editor (Fig. 1) in three linear steps:
1. Explore. The user can explore available blocks contained in the toolbox panel and divided into
various categories.
2. Drag-and-drop. It is possible to drag and drop blocks to the workspace following a guided
procedure and create a structure consisting of several interlocking blocks.
3. Export. At any time, the user can download a file in XMI format containing the code to be
imported into a UML design software and a textual report describing the structure created.
As previously mentioned, according to our method, multiple diagrams are expected to reach a
complete representation of a process. However, in developing the prototype, we chose the UML class
diagram as a starting point. Offering a preliminary overview of the process, the class diagram focuses
on the process structure, i.e., the system, by representing all the entities involved: actors, resources,
technological components, as well as operations and relations among entities.
The tool we selected for developing the block-based visual language in ModeLLer is Blockly [14].
Blockly is a JavaScript library based on a no-code/low-code approach [15] that is commonly adopted
for simple and user-friendly development environments [16]. Recent studies have proposed block-
based solutions to assist users in customising applications for monitoring and configuring agricultural
processes. [17, 18] Although Blockly provides pre-defined blocks, these are typically programming-
oriented, and additional custom blocks were necessary to enable the creation of the class diagram and
4
ModeLLer code, as well as a demo and a video presentation are available on GitHub: https://github.com/Unipisa/blockly-
UML-modeller
support users in identifying all the necessary elements. In ModeLLer, blocks represent the different
entities involved in the system and differ in colour, size and number of interlocking blocks. For example,
the orange custom block Actor represents the actor class and contains a textual attribute to be assigned
by the user. This text can be the actor’s role (e.g., farmer, employee, drone pilot, etc.). Actors must be
interlocked with one or more Activity blocks as suggested by the label placed on the element. The
creation of a diagram will be illustrated in detail in the next section.
4. A Case Study on Precision Irrigation
We selected the UML class diagram of a precision irrigation system as a case study for testing the
ModeLLer prototype. The diagram is extracted from the pilot study developed for setting up the
modelling method presented in [11]. The diagram was developed by experts on the notation based on
a report collaboratively written by the interdisciplinary team involved in the LL and includes all the
entities to be considered in representing the irrigation process, i.e., a user, a decision support system,
natural resources and irrigation tools.
Interacting with the current version of ModeLLer, it is possible to rebuild the precision irrigation class
diagram in two consecutive steps: (1) creation of the classes, attributes, operations and relationships
with the block-based editor and export of XMI code; (2) import of XMI code in a UML application and
visualisation of the diagram.
The creation of a model starts from the questions contained on the preset blocks appearing on the
initial setup of the workspace: (1) Who are the actors involved in the process? and (2) Which are the
resources?. The user can select available blocks in the toolbox and drag and drop them to the workspace.
Once an actor block is added to the workspace, the user is asked a new question: (3) What are the
activities carried out by the actor? The user is asked to provide information on activities performed
following this pattern: an ⟨actor⟩ performs an ⟨activity⟩ using ⟨resource⟩ to ⟨motivation⟩.
Advanced UML structures, such as composition, generalisation, and attributes, are also available
in the toolbox or directly on the blocks. Once the modelling is finished, as represented in Fig. 1, it is
possible to download the XMI file containing the code generated while the model is created on the
workspace. Along with the XMI code, a textual report is generated and available for download. The
textual report serves as support explaining the structure created by the user through natural language.
To obtain a diagram in the UML graphical notation, it is necessary to perform step 2, importing the
XMI file into a UML design software supporting XMI standard5 . A syntactically correct class diagram
can be created by selecting the XMI imported classes, which maintain features and relationships as
defined by the user in ModeLLer. In future releases, we will work to include, in addition to XMI code,
the graphical notation and export the image format directly from ModeLLer.
5. Evaluation and Re-design
The prototype was evaluated through the cognitive walkthrough approach [19]. This technique allowed
us to evaluate the learnability and usability of ModeLLer from the user’s point of view.
A remote workshop was conducted with a team of cross-functional evaluators to assess various
aspects of the prototype interface. This team included three males and one female expert, aged between
35 and 45, with backgrounds in formal languages, visual languages, web design, and agricultural
technology. They were guided through a series of tasks related to the modelling activity by a facilitator,
answering predefined questions. The findings of this assessment are detailed in Table 1, which lists the
actions reviewed and the experts’ responses.
The first action analysed by the group of experts was the identification of blocks within the toolbox.
Overall, the experts agreed that “Finding blocks is simple and intuitive. The menu, divided into different
categories, guides the user towards identifying the blocks”. By clicking on the label of the chosen
5
We tested XMI import in StarUML v.5.1.0 https://staruml.io/ and Visual Paradigm 17.0 https://www.visual-paradigm.com/,
last accessed 29 March 2024
Table 1
Prototype evaluation with cognitive walkthrough
Will users associate After the action
Will users try Will users notice
the correct action is performed, will users
EVALUATION to achieve that the correct action
with the result they’re see that progress is made
the right result? is available?
trying to achieve? toward the goal?
ACTION 1: Yes, there is connection
Yes, a prompt or label
Identifying Yes, from experience Yes, from experience between user goal
matches action
blocks in toolbox and system response
ACTION 2: Yes, there is connection
Yes, they would see Yes, a prompt or label
Construction Yes, from experience between user goal
a call-to-action matches action
of the model in workspace and system response
ACTION 3: Yes, there is connection
Yes, they would see Yes, a prompt or label
Creation of No between user goal
a call-to-action matches action
advanced structures and system response
Yes, there is connection
ACTION 4: Yes, they would see Yes, a prompt or label
Yes, the system tells them to between user goal
Export a call-to-action matches action
and system response
category, the system responds by opening a panel containing the blocks of the selected category. In this
way, the user receives clear feedback to have performed the correct action.
The second action analysed by the experts was the graphic construction of the model, i.e., dragging the
blocks into the workspace and fitting them together. Even for this action, the review team expressed
itself positively. The presence of a fixed block in the work area containing the phrases Insert actors and
Insert resources with space underneath for inserting something suggests the user to insert other blocks.
The fact that the words Actors and Resources are also the names of the two main categories guides the
user to open these categories in the toolbox. Once a category is opened, hovering over a block will
cause the block border to glow yellow and the cursor to a hand ready to select, move and release. Thus,
the group agreed, “It is obvious to the user that the block editor has a drag-and-drop mechanism, and
the interlocking on the blocks, similar to that of the puzzle pieces, acts as a sort of visual call-to-action”.
The third activity that was analysed pertains to the creation of advanced structures. This may
correspond to a generalization, an aggregation, or an association between two classes; the latter case
was examined during the workshop. In the prototype, as well as in traditional UML design software, to
obtain an association between two classes, both class blocks must be connected. The perplexities of the
experts concerned the insertion of the class name in a text field appearing under the label Using resource
or interacting with actor of the Custom activity block. The team agreed that “This action requires too
much cognitive effort for the user to achieve the right result”. The group thought about alternative
solutions.
The last examined action was the export of the XMI file and textual report. For the action analysed
in this step, the group was unanimously positive: “The two download buttons are standard and easily
visible, and give the idea of being clickable, as their labels make it immediately clear what action will
be performed”. When one of the two buttons is clicked, the system responds by starting the download
of the chosen file and opening the start download window in the browser; then the progress bar allows
the user to understand that the action was performed correctly and is achieving its goal.
The feedback collected during the cognitive walkthrough workshop helped us to re-design critical
emerging components. The main change was related to the third action, creation of advanced structures,
which was the only activity evaluated negatively by the experts. The solution identified consisted of
replacing the text field associated with the input Using resource or interacting with actor with a selector.
During the creation of an association between two classes, it is now possible for the user to choose the
class to be associated from a list containing the already existing classes instead of manually typing the
name of the desired class. This suggests possible relations without seeking the element name in the
diagram while limiting the user’s errors.
6. Limitations and Future Work
This research acknowledges several limitations, which provide a roadmap for future work.
Firstly, a significant limitation lies in the current stage of development. ModeLLer, which is still in
its initial development phase, offers limited functionalities at this time. Primarily, the tool is confined
to creating entity models, i.e., UML class diagrams, with additional aspects of the system, like process
modelling, requiring further enhancement. As the tool is part of a European research project, we
anticipate incorporating more features and improvements in subsequent versions.
Secondly, the preliminary evaluation was conducted with a cross-functional team of experts from fields
such as visual languages, formal languages, and agricultural technology. However, these evaluators may
not reflect the intended end-user demographic of the tool. Furthermore, the evaluation was restricted
in scope because of the tool’s early development stage, thus lacking the depth and breadth that a
comprehensive usability study might provide, including individualised feedback. Therefore, future
studies will involve key stakeholders in the evaluation stages to gain a more holistic understanding of
the tool’s usability and areas for improvement.
Thirdly, ModeLLer has been designed with a specific focus on digital agriculture, meaning that its
broader applicability across other domains has not been explored. Future research could investigate
this tool’s potential utility across various fields beyond digital agriculture.
Lastly, the integration and application of the concepts of Meta-Design and Living Labs in our work
is still at a preliminary stage. Further work is needed to deepen the integration of these concepts in
our model-driven requirements engineering techniques and to better clarify their relationship with the
broader research environment.
Moreover, in future work, it is worth exploring the potential of the tool for synergistic human-AI
co-creation through natural language interactions and generative AI.
Acknowledgment
Research partly funded by the European Union’s Horizon Europe research and innovation programme
under grant agreement no. 101060179.
References
[1] Y. Tang, S. Dananjayan, C. Hou, Q. Guo, S. Luo, Y. He, A survey on the 5g network and its impact
on agriculture: Challenges and opportunities, CEA 180 (2021).
[2] R. Bertoglio, C. Corbo, F. M. Renga, M. Matteucci, The digital agricultural revolution: A bibliometric
analysis literature review, IEEE Access 9 (2021).
[3] K. Rijswijk, L. Klerkx, M. Bacco, F. Bartolini, E. Bulten, L. Debruyne, J. Dessein, I. Scotti, G. Brunori,
Digital Transformation of Agriculture and Rural Areas: a Socio-Cyber-Physical System Framework
to Support Responsibilisation, JRS (2021).
[4] A. Ferrari, M. Bacco, K. Gaber, A. Jedlitschka, S. Hess, J. Kaipainen, P. Koltsida, E. Toli, G. Brunori,
Drivers, Barriers and Impacts of Digitalisation in Rural Areas from the Viewpoint of Experts, IST
145 (2022) 106816.
[5] CODECS, https://www.horizoncodecs.eu/, 2023.
[6] S. Leminen, M. Westerlund, A.-G. Nyström, Living labs as open-innovation networks (2012).
[7] G. Loniewski, E. Insfran, S. Abrahão, A systematic review of the use of requirements engineering
techniques in model-driven development, in: MODELS 2010, Springer, 2010, pp. 213–227.
[8] D. Moody, The “physics” of notations: Toward a scientific basis for constructing visual notations
in software engineering, IEEE Transactions on Software Engineering 35 (2009) 756–779.
[9] Unified Modeling Language (UML) 2.5.1 Core Specification, https://www.omg.org/spec/UML, 2017.
[10] G. Fischer, E. Giaccardi, Meta-design: A framework for the future of end-user development, End
user development (2006) 427–457.
[11] C. Mannari, G. O. Spagnolo, M. Bacco, A. Malizia, Digitalisation of agriculture: Development and
evaluation of a model-based requirements engineering process, in: REFSQ, 2023.
[12] C. Mannari, M. Bacco, A. Ferrari, L. Ortolani, M. B. Lai, C. Mignani, A. Silvi, A. Malizia, G. Brunori,
A methodology for process modelling in living labs to foster agricultural digitalisation, in: 2023
IEEE MetroAgriFor, 2023.
[13] G. Fischer, T. Herrmann, Socio-technical systems: A meta-design perspective, IJSKD 3 (2011) 1–33.
[14] Blockly, https://developers.google.com/blockly?hl=it, 2022.
[15] A. C. Bock, U. Frank, Low-code platform, Business & Information Systems Engineering 63 (2021)
733–740.
[16] M. Resnick, J. Maloney, A. Monroy-Hernández, N. Rusk, E. Eastmond, K. Brennan, A. Millner,
E. Rosenbaum, J. Silver, B. Silverman, et al., Scratch: programming for all, Communications of the
ACM 52 (2009) 60–67.
[17] B. M. A. Amer, H. Chouikhi, Smartphone application using a visual programming language to
compute drying/solar drying characteristics of agricultural products, Sustainability 12 (2020).
[18] G. Abraham, R. R., M. Nithya, Smart agriculture based on iot and machine learning, 2021, pp.
414–419. doi:10.1109/ICCMC51019.2021.9418392.
[19] Nielsen Norman Group, “Evaluate interface learnability with cognitive walkthroughs”, https:
//www.nngroup.com/articles/cognitive-walkthroughs, 2022.