2026
Conference article
Open Access
Designing adaptive AI assistance for block-based modelling: a Wizard of Oz study with domain experts
Mannari Chiara, Turchi Tommaso, Bacco Manlio, Ferrari Alessio, Conati Cristina, Malizia Alessio
The increasing pervasiveness of software-intensive systems requires involving domain experts more directly in technological development. Visual models, expressed in semi-formal notations, can act as shared artefacts that support communication and collaboration between developers and domain experts. However, modelling with semi-formal notations can be challenging for novice modellers. This study presents an AI-infused, web-based modelling tool designed to support users in formalising domain knowledge without requiring advanced modelling skills. The tool features a block-based, domain-specific language that automatically transforms user-generated structures into semi-formal diagrams. AI-based functionalities include a diagram reader, contextual hints, natural-language instructions, and interaction logging. We evaluated the tool through a Wizard of Oz experiment with agronomists in digital agriculture, where participants completed an exploratory modelling task while interacting with AI assistance. Results reveal three key design implications: (i) adaptive AI support accommodating diverse modelling strategies, (ii) concise, actionable guidance delivered at moments of difficulty, and (iii) practice-oriented assistance that preserves user agency and supports learning-by-doing.DOI: 10.1145/3811427.3811448
Project(s): Maximising the CO-benefits of agricultural Digitalisation through conducive digital ECoSystems
Metrics:
Mannari Chiara, Turchi Tommaso, Bacco Manlio, Ferrari Alessio, Conati Cristina, Malizia Alessio
The increasing pervasiveness of software-intensive systems requires involving domain experts more directly in technological development. Visual models, expressed in semi-formal notations, can act as shared artefacts that support communication and collaboration between developers and domain experts. However, modelling with semi-formal notations can be challenging for novice modellers. This study presents an AI-infused, web-based modelling tool designed to support users in formalising domain knowledge without requiring advanced modelling skills. The tool features a block-based, domain-specific language that automatically transforms user-generated structures into semi-formal diagrams. AI-based functionalities include a diagram reader, contextual hints, natural-language instructions, and interaction logging. We evaluated the tool through a Wizard of Oz experiment with agronomists in digital agriculture, where participants completed an exploratory modelling task while interacting with AI assistance. Results reveal three key design implications: (i) adaptive AI support accommodating diverse modelling strategies, (ii) concise, actionable guidance delivered at moments of difficulty, and (iii) practice-oriented assistance that preserves user agency and supports learning-by-doing.DOI: 10.1145/3811427.3811448
Project(s): Maximising the CO-benefits of agricultural Digitalisation through conducive digital ECoSystems
Metrics:
See at:
dl.acm.org 
| CNR IRIS | CNR IRIS 
| CNR IRIS
2025
Conference article
Open Access
A framework integrating agile software development principles for co-design and participatory cost-benefit evaluation in digital agriculture
Lepore F., Vergamini D., Ortolani L., Mannari C., Ferrari A., Bacco M., Brunori G.
Digital innovation in agriculture often struggles to meet real operational needs due to limited stakeholder involvement and insufficient assessment of context-specific costs and benefits. To bridge this gap this paper introduces AGILE- CBA, a methodological framework that (a) integrates co-design practices, (b) is structured through a Scrum Agile development process, and (c) includes a participatory cost-benefit evaluation. The framework organises co-design into a seven-step iterative cycle, embedding a five-step participatory assessment loop within each sprint. This dual structure enables the continuous and situated evaluation of both expected and observed costs and benefits, encompassing tangible and intangible aspects. By aligning key Scrum practices, such as backlog management, sprint reviews and retrospectives, with facilitated dialogue and collective reflection, AGILE-CBA can support more informed prioritisation, enhances context relevance, and reduces adoption risks. Facilitators play a crucial role in mediating communication and adjusting the pace and content of participatory activities to seasonal workloads and user capabilities. The approach is particularly suited to farming systems characterised by variability, environmental and seasonal dependency, and multi-actor complexity, offering a flexible and replicable pathway toward more inclusive, context-aware, and sustainable digital agriculture.
Lepore F., Vergamini D., Ortolani L., Mannari C., Ferrari A., Bacco M., Brunori G.
Digital innovation in agriculture often struggles to meet real operational needs due to limited stakeholder involvement and insufficient assessment of context-specific costs and benefits. To bridge this gap this paper introduces AGILE- CBA, a methodological framework that (a) integrates co-design practices, (b) is structured through a Scrum Agile development process, and (c) includes a participatory cost-benefit evaluation. The framework organises co-design into a seven-step iterative cycle, embedding a five-step participatory assessment loop within each sprint. This dual structure enables the continuous and situated evaluation of both expected and observed costs and benefits, encompassing tangible and intangible aspects. By aligning key Scrum practices, such as backlog management, sprint reviews and retrospectives, with facilitated dialogue and collective reflection, AGILE-CBA can support more informed prioritisation, enhances context relevance, and reduces adoption risks. Facilitators play a crucial role in mediating communication and adjusting the pace and content of participatory activities to seasonal workloads and user capabilities. The approach is particularly suited to farming systems characterised by variability, environmental and seasonal dependency, and multi-actor complexity, offering a flexible and replicable pathway toward more inclusive, context-aware, and sustainable digital agriculture.
2025
Journal article
Open Access
Sustainable digitalisation - a system thinking approach for determining costs and benefits in the agri-sector
Soma K., Brunori G., Giagnocavo C., Meulman F., Ryan M., Heredia Hortigüela R. M., Iliopoulos C., Paulus M., Ferrari A., Kilis E., Grando S., Bellon-Maurel V., Knierim A, Gobrecht A, Selnes T., Ortolani L., Bacco M., Mannari C.
The digital transformation of agriculture is widely promoted as a pathway to sustainability, yet the actual outcomes of digitalisation remain uncertain and context-dependent. As such, technology uptake among businesses can have positive impacts on individual farms, while the aggregated outcomes of digitalisation involving multiple farms and multi-actors in associated networks are fully uncertain. The novelty of this research is the introduction of an approach to investigate costs and benefits in different contexts at different levels of digitalisation. Objective The main objective is to introduce a systems-based approach for assessing sustainable digitalisation by differentiating outcomes across multiple levels of analysis. This approach is designed to address the common pitfall of generalising impacts such as assuming large-scale effects based on evidence limited to the farm level. Methods This research is based on a scoping literature review in the European Union Horizon Europe project called CODECS, which is highly suited for interdisciplinary research with multiple topics. Results and conclusion A framework has been designed to clarify the needs for distinguishing costs and benefits of digitalisation across three interconnected system levels: digitised socio-physical systems, socio-cyber-physical systems, and governance-cyber-ecological systems. To deal with complexities at each level, the framework integrates internal and external drivers, contextual conditions, and value-based perspectives, which all will influence outcomes of sustainability assessments. Significance The framework offers a practical tool for researchers, policymakers, and innovation actors, to deal with the complexities of digital transitions in agriculture, to reach at sustainable digitalisation outcomes in a long term regionally, as well as in a short-term locally, by enhanced understanding of the needs for distinguished sustainability assessment applications to reach at more accurate costs and benefits.Source: AGRICULTURAL SYSTEMS, vol. 231 (issue 104529)
DOI: 10.1016/j.agsy.2025.104529
Project(s): CODECS
Metrics:
Soma K., Brunori G., Giagnocavo C., Meulman F., Ryan M., Heredia Hortigüela R. M., Iliopoulos C., Paulus M., Ferrari A., Kilis E., Grando S., Bellon-Maurel V., Knierim A, Gobrecht A, Selnes T., Ortolani L., Bacco M., Mannari C.
The digital transformation of agriculture is widely promoted as a pathway to sustainability, yet the actual outcomes of digitalisation remain uncertain and context-dependent. As such, technology uptake among businesses can have positive impacts on individual farms, while the aggregated outcomes of digitalisation involving multiple farms and multi-actors in associated networks are fully uncertain. The novelty of this research is the introduction of an approach to investigate costs and benefits in different contexts at different levels of digitalisation. Objective The main objective is to introduce a systems-based approach for assessing sustainable digitalisation by differentiating outcomes across multiple levels of analysis. This approach is designed to address the common pitfall of generalising impacts such as assuming large-scale effects based on evidence limited to the farm level. Methods This research is based on a scoping literature review in the European Union Horizon Europe project called CODECS, which is highly suited for interdisciplinary research with multiple topics. Results and conclusion A framework has been designed to clarify the needs for distinguishing costs and benefits of digitalisation across three interconnected system levels: digitised socio-physical systems, socio-cyber-physical systems, and governance-cyber-ecological systems. To deal with complexities at each level, the framework integrates internal and external drivers, contextual conditions, and value-based perspectives, which all will influence outcomes of sustainability assessments. Significance The framework offers a practical tool for researchers, policymakers, and innovation actors, to deal with the complexities of digital transitions in agriculture, to reach at sustainable digitalisation outcomes in a long term regionally, as well as in a short-term locally, by enhanced understanding of the needs for distinguished sustainability assessment applications to reach at more accurate costs and benefits.Source: AGRICULTURAL SYSTEMS, vol. 231 (issue 104529)
DOI: 10.1016/j.agsy.2025.104529
Project(s): CODECS
Metrics:
See at:
Agricultural Systems | CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Journal article
Open Access
The urgency of addressing zoonotic diseases surveillance: potential opportunities considering one health approaches and common European Data spaces
Riccetti N., Signorelli S., Fanelli A., Massaro E., Bacco M., Szewczyk W., Ibarreta D., Ciscar J. C., Cescatti A., Coecke S., Capua I.
Currently, transdisciplinary data from animal surveillance that are available for One Health approaches to public health are scarce, negatively impacting our ability to anticipate and prepare for future public health threats, particularly those involving zoonotic diseases with pandemic or epidemic potential. In this article, we explore the potential of the common European Data Spaces framework to enhance the availability of animal surveillance data, in order to better address public health threats. We propose building upon and expanding existing initiatives, such as the European Data Spaces for Health, Agriculture, and Green Deal, to design innovative services. These services could enable the integration of different data sources to inform research and policymaking on public health interventions. An overarching layer, populated with data and generating integrative information, could support a One Health approach to research and policymaking for the preparedness and anticipation of zoonotic diseases. Consequently, this approach might foster data sharing from Member States by leveraging existing developments within data spaces in terms of, for example, data security. It could also support researchers and developers in accessing transdisciplinary, stratified, and quality-controlled data for their projects.Source: DATA IN BRIEF, vol. 59 (issue 111332)
DOI: 10.1016/j.dib.2025.111332
Metrics:
Riccetti N., Signorelli S., Fanelli A., Massaro E., Bacco M., Szewczyk W., Ibarreta D., Ciscar J. C., Cescatti A., Coecke S., Capua I.
Currently, transdisciplinary data from animal surveillance that are available for One Health approaches to public health are scarce, negatively impacting our ability to anticipate and prepare for future public health threats, particularly those involving zoonotic diseases with pandemic or epidemic potential. In this article, we explore the potential of the common European Data Spaces framework to enhance the availability of animal surveillance data, in order to better address public health threats. We propose building upon and expanding existing initiatives, such as the European Data Spaces for Health, Agriculture, and Green Deal, to design innovative services. These services could enable the integration of different data sources to inform research and policymaking on public health interventions. An overarching layer, populated with data and generating integrative information, could support a One Health approach to research and policymaking for the preparedness and anticipation of zoonotic diseases. Consequently, this approach might foster data sharing from Member States by leveraging existing developments within data spaces in terms of, for example, data security. It could also support researchers and developers in accessing transdisciplinary, stratified, and quality-controlled data for their projects.Source: DATA IN BRIEF, vol. 59 (issue 111332)
DOI: 10.1016/j.dib.2025.111332
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Conference article
Open Access
Assisting stakeholders in class diagram interpretation with LLMs: a work in progress
Mannari C., Turchi T., Bacco M., Malizia A.
Diagrams can be valuable tools in requirements engineering to establish a shared understanding between software engineers and stakeholders. However, interacting with these visual representations can be challenging for some stakeholders who prefer textual descriptions and may need support to inter- pret notation elements and understand the diagram structure and meaning. To address this need, we explore the use of Large Language Models to effectively assist stakeholders interacting with diagrams by providing automatic textual explanations and contextual guidance. Specifically, we aim to design and evaluate with stakeholders an interactive layer (integrated into an end- user-oriented modelling tool) that provides automatic diagram explanations in natural language. As a first step toward our research objective, this paper investigates the capability of GPT4 to generate appropriate textual descriptions from domain models. We use a test data set consisting of UML class diagrams in various formats, belonging to the domain of digital agriculture, and develop a set of prompts to generate the interactive ex- planatory layer. We conduct a technical evaluation of the output, focusing on correctness, completeness, and understandability. The results provide valuable insights to inform future design and research, while also revealing potential challenges in real-world applications.DOI: 10.1109/rew66121.2025.00045
Metrics:
Mannari C., Turchi T., Bacco M., Malizia A.
Diagrams can be valuable tools in requirements engineering to establish a shared understanding between software engineers and stakeholders. However, interacting with these visual representations can be challenging for some stakeholders who prefer textual descriptions and may need support to inter- pret notation elements and understand the diagram structure and meaning. To address this need, we explore the use of Large Language Models to effectively assist stakeholders interacting with diagrams by providing automatic textual explanations and contextual guidance. Specifically, we aim to design and evaluate with stakeholders an interactive layer (integrated into an end- user-oriented modelling tool) that provides automatic diagram explanations in natural language. As a first step toward our research objective, this paper investigates the capability of GPT4 to generate appropriate textual descriptions from domain models. We use a test data set consisting of UML class diagrams in various formats, belonging to the domain of digital agriculture, and develop a set of prompts to generate the interactive ex- planatory layer. We conduct a technical evaluation of the output, focusing on correctness, completeness, and understandability. The results provide valuable insights to inform future design and research, while also revealing potential challenges in real-world applications.DOI: 10.1109/rew66121.2025.00045
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Beyond prototypes: what is missing to fill the gaps in IoT-enabled hydroponics platforms
Sportelli M., La Rosa D., Crivello A., Pineda-Medina Dunia, Bacco M., Barsocchi P.
Hydroponic agriculture, when combined with Internet of Things (IoT) technologies, provides a promising pathway to sustainable and efficient food production. This paper aims to systematically review and analyze recent advancements in IoT-based management for hydroponic systems, with a particular focus on assessing the technological maturity of current solutions, identifying existing gaps, and outlining promising directions for future research and development. Based on a review of 74 recent studies, the findings reveal a fragmented landscape characterized by custom-built solutions, predominantly relying on open-source microcontrollers and WiFi connectivity, but with limited adoption of standardized protocols and interoperable platforms. The majority of applications emphasize monitoring of core hydroponic parameters such as pH, EC, and temperature, while emerging uses of machine learning remain at an early stage. Few systems demonstrate readiness for commercial deployment or integration within broader smart agriculture ecosystems. By clarifying the current state of IoT-enabled hydroponics, this review highlights both the opportunities and the challenges in advancing from isolated prototypes toward robust, scalable systems capable of real-world application.Source: HORTICULTURAE, vol. 11 (issue 11)
DOI: 10.3390/horticulturae11111322
Metrics:
Sportelli M., La Rosa D., Crivello A., Pineda-Medina Dunia, Bacco M., Barsocchi P.
Hydroponic agriculture, when combined with Internet of Things (IoT) technologies, provides a promising pathway to sustainable and efficient food production. This paper aims to systematically review and analyze recent advancements in IoT-based management for hydroponic systems, with a particular focus on assessing the technological maturity of current solutions, identifying existing gaps, and outlining promising directions for future research and development. Based on a review of 74 recent studies, the findings reveal a fragmented landscape characterized by custom-built solutions, predominantly relying on open-source microcontrollers and WiFi connectivity, but with limited adoption of standardized protocols and interoperable platforms. The majority of applications emphasize monitoring of core hydroponic parameters such as pH, EC, and temperature, while emerging uses of machine learning remain at an early stage. Few systems demonstrate readiness for commercial deployment or integration within broader smart agriculture ecosystems. By clarifying the current state of IoT-enabled hydroponics, this review highlights both the opportunities and the challenges in advancing from isolated prototypes toward robust, scalable systems capable of real-world application.Source: HORTICULTURAE, vol. 11 (issue 11)
DOI: 10.3390/horticulturae11111322
Metrics:
See at:
Horticulturae | CNR IRIS | www.mdpi.com | CNR IRIS
2025
Journal article
Open Access
Agri-Food Data Spaces: highlighting the need for a farm-centered strategy
Brunori G., Bacco M., Puerta Pinero C., Borzacchiello M. T., Stormer E.
This paper explores the potential of digitalisation in agriculture to improve the sustainability of agriculture production and industrial sectors, contributing to the twin digital and green transition. These systems can facilitate and enhance competitiveness by leveraging on mutually reinforcing transformations. The European Commission has proposed the creation of Common European Data Spaces in specific sectors to support such a transition. We focus on the agri-food domain, considering farmers and other actors in the food chain. The aim is to identify needs, priorities, opportunities, and barriers to a Common European Data Space for agriculture and food systems, thus going beyond the sectoral European Data Space for agriculture already under current development. In addition, this work looks at strategies for introducing the aforementioned novel data space and evidence of benefits for farmers, who are a key component of agricultural and food systems. To accomplish this, the concept of data spaces is presented, analysing main components, functions, and potential challenges and opportunities for data sharing and reuse, with the agri-food context as the main focus. It also presents current and future scenarios for data use at different decision-making levels, focusing on the specific role of farmers in the digital ecosystem. Additionally, it outlines the basic principles for an inclusive agri-food data strategy.Source: DATA IN BRIEF, vol. 59 (issue 111388)
DOI: 10.1016/j.dib.2025.111388
Metrics:
Brunori G., Bacco M., Puerta Pinero C., Borzacchiello M. T., Stormer E.
This paper explores the potential of digitalisation in agriculture to improve the sustainability of agriculture production and industrial sectors, contributing to the twin digital and green transition. These systems can facilitate and enhance competitiveness by leveraging on mutually reinforcing transformations. The European Commission has proposed the creation of Common European Data Spaces in specific sectors to support such a transition. We focus on the agri-food domain, considering farmers and other actors in the food chain. The aim is to identify needs, priorities, opportunities, and barriers to a Common European Data Space for agriculture and food systems, thus going beyond the sectoral European Data Space for agriculture already under current development. In addition, this work looks at strategies for introducing the aforementioned novel data space and evidence of benefits for farmers, who are a key component of agricultural and food systems. To accomplish this, the concept of data spaces is presented, analysing main components, functions, and potential challenges and opportunities for data sharing and reuse, with the agri-food context as the main focus. It also presents current and future scenarios for data use at different decision-making levels, focusing on the specific role of farmers in the digital ecosystem. Additionally, it outlines the basic principles for an inclusive agri-food data strategy.Source: DATA IN BRIEF, vol. 59 (issue 111388)
DOI: 10.1016/j.dib.2025.111388
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Conference article
Restricted
Assessing computational thinking skills through artefacts: the case of modeLLer
Mannari C., Turchi T., Frosali C., Bacco M., Ferrari A., Conati C., Malizia A.
Computational thinking (CT) skills provide structured approaches to problem-solving that are valuable for navigating the increasing complexity of technological environments. CT skills can be assessed through various methods and perspectives. EUDability provides a framework for evaluating end-user development (EUD) tools, with core dimensions directly aligned to CT skills. This paper explores how CT skills manifest in the creation of visual models, an activity that supports the representation and understanding of socio-technical systems. We propose an evaluation method employing ModeLLer, a block-based EUD modelling tool and a user study. We carry out a structured evaluation integrating the EUDability inspection and process-based CT skills evaluation with the assessment of the artefacts produced by end-users. Results highlight the ability of ModeLLer to support the modelling activity while also providing insights into the EUDability of the tool and end-users’ CT skills. Furthermore, although preliminary, our results illustrate the relationship between tool capabilities, user skills, and modelling outcomes.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15713, pp. 312-321. Munich, Germany, 16-18/06/2025
DOI: 10.1007/978-3-031-95452-8_19
Project(s): CODECS
Metrics:
Mannari C., Turchi T., Frosali C., Bacco M., Ferrari A., Conati C., Malizia A.
Computational thinking (CT) skills provide structured approaches to problem-solving that are valuable for navigating the increasing complexity of technological environments. CT skills can be assessed through various methods and perspectives. EUDability provides a framework for evaluating end-user development (EUD) tools, with core dimensions directly aligned to CT skills. This paper explores how CT skills manifest in the creation of visual models, an activity that supports the representation and understanding of socio-technical systems. We propose an evaluation method employing ModeLLer, a block-based EUD modelling tool and a user study. We carry out a structured evaluation integrating the EUDability inspection and process-based CT skills evaluation with the assessment of the artefacts produced by end-users. Results highlight the ability of ModeLLer to support the modelling activity while also providing insights into the EUDability of the tool and end-users’ CT skills. Furthermore, although preliminary, our results illustrate the relationship between tool capabilities, user skills, and modelling outcomes.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15713, pp. 312-321. Munich, Germany, 16-18/06/2025
DOI: 10.1007/978-3-031-95452-8_19
Project(s): CODECS
Metrics:
See at:
CNR IRIS | CNR IRIS | link.springer.com
2025
Other
Open Access
CODECS Deliverable D3.2. Analysis of Digital Ecosystems
Giagnocavo C., Hortigüela R. M. H., Olmedo Osuna L., Knierim A., Herrera B., Ferrari A., Mannari C., Bacco M.
This Deliverable 3.1 provides an in-depth analysis of Digital Ecosystems (DEs) across 19 Living Labs (LLs) established under the CODECS project. It constitutes a central output of Work Package 3 (WP3), which examines how socio-ecological conditions shape agricultural digitalisation processes. The document builds upon the initial version of D3.1 delivered at M24, extending it with a more detailed and validated description of the DEs. As such, it lays the foundations for the forthcoming Deliverable 3.2 (Comparative Assessment of Digital Ecosystems, due at M44), where a typology of DEs and a comparative evaluation of farm digital readiness, scaling readiness and digital ecosystem conduciveness across Europe will be developed. The results of D3.1 are based on a participatory methodology involving workshops, interviews and co-creation sessions with the 19 Living Labs. Each LL identified its Focal Action Situation (FAS), the concrete problem statement around which actors, resources and governance systems interact, and mapped the RCCIs required to address it. Data were subsequently coded and analysed enabling a context-sensitive characterisation of DEs for each LL.DOI: 10.5281/zenodo.17235792
Project(s): CODECS
Metrics:
Giagnocavo C., Hortigüela R. M. H., Olmedo Osuna L., Knierim A., Herrera B., Ferrari A., Mannari C., Bacco M.
This Deliverable 3.1 provides an in-depth analysis of Digital Ecosystems (DEs) across 19 Living Labs (LLs) established under the CODECS project. It constitutes a central output of Work Package 3 (WP3), which examines how socio-ecological conditions shape agricultural digitalisation processes. The document builds upon the initial version of D3.1 delivered at M24, extending it with a more detailed and validated description of the DEs. As such, it lays the foundations for the forthcoming Deliverable 3.2 (Comparative Assessment of Digital Ecosystems, due at M44), where a typology of DEs and a comparative evaluation of farm digital readiness, scaling readiness and digital ecosystem conduciveness across Europe will be developed. The results of D3.1 are based on a participatory methodology involving workshops, interviews and co-creation sessions with the 19 Living Labs. Each LL identified its Focal Action Situation (FAS), the concrete problem statement around which actors, resources and governance systems interact, and mapped the RCCIs required to address it. Data were subsequently coded and analysed enabling a context-sensitive characterisation of DEs for each LL.DOI: 10.5281/zenodo.17235792
Project(s): CODECS
Metrics:
2025
Conference article
Open Access
End-user requirements modelling: an experience report from digital agriculture
Mannari C., Sportelli M., Meesala H., Okoye O. F., Lepore F., Bacco M., Brunori G., Malizia A., Ferrari A.
Context and motivation: End-user development focuses on enabling non-professional programmers to create or extend software applications on their own. However, before beginning the development process, software engineering best practices recommend performing requirements engineering (RE) activities, including requirements modelling.Question/problem: There is limited research on how end-users can model system requirements. Principal ideas/results: In this experience report, we investigate the problem of end-user requirements modelling in an EU-funded project about agricultural digitalisation. Specifically, a team of agronomists was directly involved in the creation of UML, iStar, and BPMN diagrams to model the transformation of socio-technical processes in four different concrete scenarios. They followed a formalisation procedure proposed within an RE method designed to help stakeholders evaluate the impact of agricultural digitalisation. Starting from textual reports including a description of the process as-is and the process-to-be, they followed step-by-step guidelines for model creation. Contribution: This paper reports insights from the experience from the viewpoint of the agronomists and software engineers involved. We identify nine key lessons that highlight the added value of end-user requirements modelling for achieving a shared and in-depth understanding of the socio-technical processes under analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15588, pp. 304-316. Barcellona, Spain, 2025
DOI: 10.1007/978-3-031-88531-0_22
Project(s): CODECS
Metrics:
Mannari C., Sportelli M., Meesala H., Okoye O. F., Lepore F., Bacco M., Brunori G., Malizia A., Ferrari A.
Context and motivation: End-user development focuses on enabling non-professional programmers to create or extend software applications on their own. However, before beginning the development process, software engineering best practices recommend performing requirements engineering (RE) activities, including requirements modelling.Question/problem: There is limited research on how end-users can model system requirements. Principal ideas/results: In this experience report, we investigate the problem of end-user requirements modelling in an EU-funded project about agricultural digitalisation. Specifically, a team of agronomists was directly involved in the creation of UML, iStar, and BPMN diagrams to model the transformation of socio-technical processes in four different concrete scenarios. They followed a formalisation procedure proposed within an RE method designed to help stakeholders evaluate the impact of agricultural digitalisation. Starting from textual reports including a description of the process as-is and the process-to-be, they followed step-by-step guidelines for model creation. Contribution: This paper reports insights from the experience from the viewpoint of the agronomists and software engineers involved. We identify nine key lessons that highlight the added value of end-user requirements modelling for achieving a shared and in-depth understanding of the socio-technical processes under analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15588, pp. 304-316. Barcellona, Spain, 2025
DOI: 10.1007/978-3-031-88531-0_22
Project(s): CODECS
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
What are data spaces? Systematic survey and future outlook
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
Bacco M., Kocian A., Chessa S., Crivello A., Barsocchi P.
Data spaces, a novel concept pushing data sharing and exchange, are experi- encing momentum because of recent developments motivated by the increas- ing need for interoperability and data sovereignty. After an initial phase, dating back to approximately twenty years ago, in which this concept has been tentatively explored in different scenarios, it is presently going through a consolidation phase in which both specifications and implementations con- verge towards a common reference for standardisation. In this context, we offer our view on data spaces by presenting a systematic literature survey, a description of the components needed to build them, how they work, and of existing mature software implementations. We thoroughly present the architectural vision behind the concept and we analyse the Reference Archi- tectural Model by IDS. We provide practical pointers to readers interested in experimenting with software components used in data spaces, and we con- clude by highlighting open challenges for their success.Source: DATA IN BRIEF
DOI: 10.1016/j.dib.2024.110969
Project(s): CODECS
Metrics:
See at:
Data in Brief | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Journal article
Open Access
Public irrigation decision support systems (IDSS) in Italy: description, evaluation and national context overview
Sportelli M., Crivello A., Bacco M., Rallo G., Brunori G.
This survey comprehensively examines the public irrigation decision support systems (IDSS) in Italy, offering a detailed description, analysis and evaluation of their features. The study investigates the agrometeorological networks and infrastructures that support Italian IDSS, providing a clearer understanding of the national context. The evaluation criteria include relevant factors such as soil moisture monitoring, crop water requirements (CWR) estimation models, biophysical parameters along with their spatial and temporal resolutions, irrigation planning and decision support visualization. Additionally, the assessment covers accessibility, scalability and interoperability of these systems. The survey also highlights the strengths and weaknesses of various IDSS, such as IRRIFRAME, IRRISIAS and IRTO, discussing their operational methodologies, data integration and regional coverage. The aim is to provide insights that facilitate advancements in sustainable irrigation management practices and address key challenges for future developments at both regional and national levels. This comprehensive evaluation seeks to enhance the effectiveness of IDSS in promoting sustainable water management in agriculture across Italy.Source: SMART AGRICULTURAL TECHNOLOGY, vol. 9
DOI: 10.1016/j.atech.2024.100564
Metrics:
Sportelli M., Crivello A., Bacco M., Rallo G., Brunori G.
This survey comprehensively examines the public irrigation decision support systems (IDSS) in Italy, offering a detailed description, analysis and evaluation of their features. The study investigates the agrometeorological networks and infrastructures that support Italian IDSS, providing a clearer understanding of the national context. The evaluation criteria include relevant factors such as soil moisture monitoring, crop water requirements (CWR) estimation models, biophysical parameters along with their spatial and temporal resolutions, irrigation planning and decision support visualization. Additionally, the assessment covers accessibility, scalability and interoperability of these systems. The survey also highlights the strengths and weaknesses of various IDSS, such as IRRIFRAME, IRRISIAS and IRTO, discussing their operational methodologies, data integration and regional coverage. The aim is to provide insights that facilitate advancements in sustainable irrigation management practices and address key challenges for future developments at both regional and national levels. This comprehensive evaluation seeks to enhance the effectiveness of IDSS in promoting sustainable water management in agriculture across Italy.Source: SMART AGRICULTURAL TECHNOLOGY, vol. 9
DOI: 10.1016/j.atech.2024.100564
Metrics:
See at:
Smart Agricultural Technology | Smart Agricultural Technology | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Conference article
Open Access
ModeLLer – Enabling end-users to model systems: a case study in digital agriculture
Mannari C., Anichini E., Bacco M., Ferrari A., Turchi T., Malizia A.
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.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3685. Arenzano, Italy, 4/06/2024
Project(s): CODECS
Mannari C., Anichini E., Bacco M., Ferrari A., Turchi T., Malizia A.
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.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3685. Arenzano, Italy, 4/06/2024
Project(s): CODECS
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Agricultural Data Space: the METRIQA platform and a case study in the CODECS project
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
Bacco M., Dimitri G. M., Kocian A., Barsocchi P., Crivello A., Brunori G., Gori M., Chessa S.
This work describes the ongoing design and devel- opment of the METRIQA platform, hosting the Italian agrifood data space. Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities. We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy. To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 543-548. Belgrade, Serbia, 8-11/09/2024
DOI: 10.15439/2024f5291
Metrics:
See at:
annals-csis.org | Annals of computer science and information systems | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Federated learning for data spaces: a privacy-enhancing strategy based on data visiting
Bacco M., Di Leo M., Kona A., Santoro M., Mazzetti P.
This work explores the paradigm of data visiting that, through privacy-enhancing technologies, shows the potential to access and use data otherwise inaccessible. Building on the ongoing EU initiative to design, implement, and run sectorial data spaces, we consider federated learning as one the most promising approaches for the objective above. We propose a domain-agnostic strategy that can be extended and adapted to different needs. We conclude by analysing the limitations and challenges of the approach we propose.DOI: 10.1109/metroagrifor63043.2024.10948754
Metrics:
Bacco M., Di Leo M., Kona A., Santoro M., Mazzetti P.
This work explores the paradigm of data visiting that, through privacy-enhancing technologies, shows the potential to access and use data otherwise inaccessible. Building on the ongoing EU initiative to design, implement, and run sectorial data spaces, we consider federated learning as one the most promising approaches for the objective above. We propose a domain-agnostic strategy that can be extended and adapted to different needs. We conclude by analysing the limitations and challenges of the approach we propose.DOI: 10.1109/metroagrifor63043.2024.10948754
Metrics:
See at:
IRIS Cnr | ieeexplore.ieee.org | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | Software Heritage | doi.org | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | GitHub | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
An IoT Platform for smart hydroponics: building blocks and open challenges
Sportelli M., Crivello A., La Rosa D., Bacco M., Incrocci L., Barsocchi P.
Hydroponics addresses inefficiencies in traditional soil-based farming by optimizing water, nutrients, and pesticide use. This method has the potential to boost crop yields and significantly reduce water consumption, tackling issues of inefficient irrigation and fertilization. This study presents the development of an IoT-based platform designed to optimize the management of smart hydroponic systems. The proposed platform facilitates real-time monitoring of key environmental parameters for hydroponic farming as well as automatic regulation of greenhouse conditions, such as temperature, humidity and nutrient levels. The platform leverages middleware software to ensure seamless communication and data management, enabling efficient decision-making processes. The primary aim of this work is to enhance the productivity and efficiency of hydroponic systems through a scalable and user-friendly solution by integrating different sensors and actuators that could be accessible through both web and mobile applications. The platform's open and flexible architecture supports the integration of advanced sensing technologies and artificial intelligence, contributing to the digitalization of agriculture and promoting environmental sustainability.DOI: 10.1109/metroagrifor63043.2024.10948780
Project(s): CODECS
Metrics:
Sportelli M., Crivello A., La Rosa D., Bacco M., Incrocci L., Barsocchi P.
Hydroponics addresses inefficiencies in traditional soil-based farming by optimizing water, nutrients, and pesticide use. This method has the potential to boost crop yields and significantly reduce water consumption, tackling issues of inefficient irrigation and fertilization. This study presents the development of an IoT-based platform designed to optimize the management of smart hydroponic systems. The proposed platform facilitates real-time monitoring of key environmental parameters for hydroponic farming as well as automatic regulation of greenhouse conditions, such as temperature, humidity and nutrient levels. The platform leverages middleware software to ensure seamless communication and data management, enabling efficient decision-making processes. The primary aim of this work is to enhance the productivity and efficiency of hydroponic systems through a scalable and user-friendly solution by integrating different sensors and actuators that could be accessible through both web and mobile applications. The platform's open and flexible architecture supports the integration of advanced sensing technologies and artificial intelligence, contributing to the digitalization of agriculture and promoting environmental sustainability.DOI: 10.1109/metroagrifor63043.2024.10948780
Project(s): CODECS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS
2024
Conference article
Open Access
Towards a toolkit for socio-technical process modelling in agriculture: a pilot study
Mannari C., Sportelli M., Okoye O. F., Bacco M., Ferrari A., Malizia A., Brunori G.
Digital technologies are transforming agriculture, affecting social, institutional, economic, environmental, and technological dimensions. To ensure sustainable development, it is essential to anticipate these impacts and create conditions for sustainable change. Living Labs (LLs) concept facilitates this by involving various stakeholders in co-designing solutions. This paper presents a socio-technical process modelling method using Model-driven requirements engineering (MoDRE) techniques. It employs UML class diagrams, iStar diagrams, and BPMN diagrams to model process structures, goals, and flows. The method, part of the Horizon Europe project CODECS, involves data collection, diagram design, and iterative feedback, tested in a precision irrigation pilot study in Tuscany. Preliminary results demonstrate the method's effectiveness in supporting interdisciplinary teams, fostering better communication, and aiding in the analysis of digitalisation impacts on agricultural processes. Furthermore, the discussion with stakeholders allowed the fine-tuning of the models and enriched the method for co-creating the diagrams with a toolkit composed of a set of guidelines for eliciting process-relevant information from LLs, a checklist and a detailed procedure for graphical representation.DOI: 10.1109/metroagrifor63043.2024.10948866
Metrics:
Mannari C., Sportelli M., Okoye O. F., Bacco M., Ferrari A., Malizia A., Brunori G.
Digital technologies are transforming agriculture, affecting social, institutional, economic, environmental, and technological dimensions. To ensure sustainable development, it is essential to anticipate these impacts and create conditions for sustainable change. Living Labs (LLs) concept facilitates this by involving various stakeholders in co-designing solutions. This paper presents a socio-technical process modelling method using Model-driven requirements engineering (MoDRE) techniques. It employs UML class diagrams, iStar diagrams, and BPMN diagrams to model process structures, goals, and flows. The method, part of the Horizon Europe project CODECS, involves data collection, diagram design, and iterative feedback, tested in a precision irrigation pilot study in Tuscany. Preliminary results demonstrate the method's effectiveness in supporting interdisciplinary teams, fostering better communication, and aiding in the analysis of digitalisation impacts on agricultural processes. Furthermore, the discussion with stakeholders allowed the fine-tuning of the models and enriched the method for co-creating the diagrams with a toolkit composed of a set of guidelines for eliciting process-relevant information from LLs, a checklist and a detailed procedure for graphical representation.DOI: 10.1109/metroagrifor63043.2024.10948866
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Towards a method for modelling socio-technical process transformation in digital agriculture
Mannari C., Bacco M., Spagnolo G. O., Malizia A., Ferrari A.
[Context and motivation] Digitalisation in agriculture is a socio-technical process that involves multiple stakeholders with diverse backgrounds and skills, e.g., in farming or technology. Capturing process transformation requires focusing on different dimensions, i.e., system structure, process flow, and actors' goals. Model-driven requirements engineering (MoDRE) techniques can offer the means to elicit and represent this multi-dimensional information. [Question/problem] This paper explores how MoDRE techniques can facilitate information exchange within interdisciplinary teams engaged in agricultural process transformations driven by digitalisation. [Principal ideas/results] We present a preliminary method for socio-technical process modelling consisting of (i) a set of different MoDRE diagrams, namely UML, iStar, and BPMN, and (ii) a procedure to collect the data required for the definition of the diagrams. The method is developed according to design science, and is currently evaluated through an action research study in the context of a living lab (LL, i.e., a network of stakeholders involved in a common socio-technical system) belonging to the agricultural domain. The evaluation with agronomists, practitioners, domain experts, and software engineers shows that the models developed are effective and understandable. Furthermore, the discussion over the completeness of the diagrams led to improved versions of the representations, considering different dimensions of the process transformation. [Contribution] There is little empirical evidence on the use of MoDRE techniques in real-world environments. This study fills this gap by developing a preliminary method for socio-technical process modelling in co-design contexts. The presented evaluation confirms the feasibility of the proposal.DOI: 10.1109/rew61692.2024.00046
DOI: 10.5281/zenodo.14005930
DOI: 10.5281/zenodo.14005929
Project(s): CODECS
Metrics:
Mannari C., Bacco M., Spagnolo G. O., Malizia A., Ferrari A.
[Context and motivation] Digitalisation in agriculture is a socio-technical process that involves multiple stakeholders with diverse backgrounds and skills, e.g., in farming or technology. Capturing process transformation requires focusing on different dimensions, i.e., system structure, process flow, and actors' goals. Model-driven requirements engineering (MoDRE) techniques can offer the means to elicit and represent this multi-dimensional information. [Question/problem] This paper explores how MoDRE techniques can facilitate information exchange within interdisciplinary teams engaged in agricultural process transformations driven by digitalisation. [Principal ideas/results] We present a preliminary method for socio-technical process modelling consisting of (i) a set of different MoDRE diagrams, namely UML, iStar, and BPMN, and (ii) a procedure to collect the data required for the definition of the diagrams. The method is developed according to design science, and is currently evaluated through an action research study in the context of a living lab (LL, i.e., a network of stakeholders involved in a common socio-technical system) belonging to the agricultural domain. The evaluation with agronomists, practitioners, domain experts, and software engineers shows that the models developed are effective and understandable. Furthermore, the discussion over the completeness of the diagrams led to improved versions of the representations, considering different dimensions of the process transformation. [Contribution] There is little empirical evidence on the use of MoDRE techniques in real-world environments. This study fills this gap by developing a preliminary method for socio-technical process modelling in co-design contexts. The presented evaluation confirms the feasibility of the proposal.DOI: 10.1109/rew61692.2024.00046
DOI: 10.5281/zenodo.14005930
DOI: 10.5281/zenodo.14005929
Project(s): CODECS
Metrics:
See at:
ZENODO | CNR IRIS | ieeexplore.ieee.org | ZENODO | doi.org | ZENODO | CNR IRIS | CNR IRIS
2024
Conference article
Restricted
Needs, challenges and opportunities of digital livestock farming in a cheese supply chain
Lepore F., Ortolani L., Ferrari A., Bacco M., Brunori G.
Digitalisation in agriculture represents a transformative change towards the exploitation of advanced technologies to improve the productivity and sustainability of farming practices. This paper explores the needs, challenges and opportunities related to the implementation of digital livestock farming in the context of Pecorino Toscano PDO cheese production, focusing on improving the efficiency and sustainability of sheep farming practices in Tuscany. Using a Living Lab approach, stakeholders collaborated to design an FMIS prototype and assess its suitability to address the specific issues of this sector. This tool supports agricultural operations and improves data-driven decision-making, embedding functionality for herd management, veterinary assistance, livestock performance monitoring and agronomic support. The use case methodology led to defining goals for improving farm-level operations and supervising the supply chain, leveraging secure data collection, integration and sharing between stakeholders. Challenges include cultural resistance to innovation, data privacy concerns, and technical and economic barriers. Opportunities include operational advantages, technology appeal, market demands for product traceability, and institutional support for digitalisation. This work suggests that digitalisation can offer promising paths to modernise sheep farming practices in specific territorial contexts, mitigate environmental impacts and improve overall farm management in the Pecorino Toscano PDO supply chain, and looks at addressing issues through collaborative approaches to trigger change.DOI: 10.1109/metroagrifor63043.2024.10948808
Project(s): CODECS
Metrics:
Lepore F., Ortolani L., Ferrari A., Bacco M., Brunori G.
Digitalisation in agriculture represents a transformative change towards the exploitation of advanced technologies to improve the productivity and sustainability of farming practices. This paper explores the needs, challenges and opportunities related to the implementation of digital livestock farming in the context of Pecorino Toscano PDO cheese production, focusing on improving the efficiency and sustainability of sheep farming practices in Tuscany. Using a Living Lab approach, stakeholders collaborated to design an FMIS prototype and assess its suitability to address the specific issues of this sector. This tool supports agricultural operations and improves data-driven decision-making, embedding functionality for herd management, veterinary assistance, livestock performance monitoring and agronomic support. The use case methodology led to defining goals for improving farm-level operations and supervising the supply chain, leveraging secure data collection, integration and sharing between stakeholders. Challenges include cultural resistance to innovation, data privacy concerns, and technical and economic barriers. Opportunities include operational advantages, technology appeal, market demands for product traceability, and institutional support for digitalisation. This work suggests that digitalisation can offer promising paths to modernise sheep farming practices in specific territorial contexts, mitigate environmental impacts and improve overall farm management in the Pecorino Toscano PDO supply chain, and looks at addressing issues through collaborative approaches to trigger change.DOI: 10.1109/metroagrifor63043.2024.10948808
Project(s): CODECS
Metrics:
See at:
doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2023
Contribution to conference
Open Access
Digitalisation of agriculture: development and evaluation of a model-based requirements engineering process
Mannari C, Spagnolo Go, Bacco M, Malizia A
[Context and Motivation] The requirements elicitation process for socio-technical systems requires the involvement of diverse stakeholders with different backgrounds and skills. In these contexts, ef- fective communication between business analysts and stakeholders can be supported by model-based requirements engineering (MoDRE) strategies, which leverage diagrammatic notations as a means for information exchange. [Question/Problem] Several diagrams and approaches exist to facilitate MoDRE. However, empirical evidence on their applicability to real-world contexts with a relevant social com- ponent, and going through a process of digitalisation, is limited. Furthermore, existing approaches do not evaluate the impact that the deployment of a novel digital system has on the process and its actors. [Principal idea/Results] The research outlined in this paper aims to evaluate the joint usage of typical requirements engineer notations, namely i*, class diagrams, and business process models in the elicitation of requirements for socially-intensive systems that are going through a transformative digitalisation process. We apply these notations to represent the system-as-is, and the system-to-be, with the goal of also evaluating the impact of digitalisation. We focus on living labs (LL, i.e., networks of stakeholders participating in a socio-technical system) belonging to the agriculture domain, and provide a preliminary application on a farm that is introducing an AI-based irrigation system. [Contribution] The results show that effective communication with non-technical stakeholders is feasible with the envisioned approach. Although multiple iterations are required, agronomists and farmers are able to provide constructive feedback on the basis of the models. Furthermore, impacts in terms of additional/removed tasks and actors can be effectively characterised through business process models. As part of our overall project, we will refine the method, and then apply it in 20 living labs in the EU.Source: CEUR WORKSHOP PROCEEDINGS. Barcelona, Spain, 17-20/04/2023
Mannari C, Spagnolo Go, Bacco M, Malizia A
[Context and Motivation] The requirements elicitation process for socio-technical systems requires the involvement of diverse stakeholders with different backgrounds and skills. In these contexts, ef- fective communication between business analysts and stakeholders can be supported by model-based requirements engineering (MoDRE) strategies, which leverage diagrammatic notations as a means for information exchange. [Question/Problem] Several diagrams and approaches exist to facilitate MoDRE. However, empirical evidence on their applicability to real-world contexts with a relevant social com- ponent, and going through a process of digitalisation, is limited. Furthermore, existing approaches do not evaluate the impact that the deployment of a novel digital system has on the process and its actors. [Principal idea/Results] The research outlined in this paper aims to evaluate the joint usage of typical requirements engineer notations, namely i*, class diagrams, and business process models in the elicitation of requirements for socially-intensive systems that are going through a transformative digitalisation process. We apply these notations to represent the system-as-is, and the system-to-be, with the goal of also evaluating the impact of digitalisation. We focus on living labs (LL, i.e., networks of stakeholders participating in a socio-technical system) belonging to the agriculture domain, and provide a preliminary application on a farm that is introducing an AI-based irrigation system. [Contribution] The results show that effective communication with non-technical stakeholders is feasible with the envisioned approach. Although multiple iterations are required, agronomists and farmers are able to provide constructive feedback on the basis of the models. Furthermore, impacts in terms of additional/removed tasks and actors can be effectively characterised through business process models. As part of our overall project, we will refine the method, and then apply it in 20 living labs in the EU.Source: CEUR WORKSHOP PROCEEDINGS. Barcelona, Spain, 17-20/04/2023
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS | CNR IRIS