2026
Contribution to book
Open Access
Formal methods for railway systems: a survey of research and technology transfer projects
Basile Davide, Ter Beek Maurice Henri, Broccia Giovanna, Gnesi Stefania, Mazzanti Franco, Spagnolo Giorgio Oronzo, Bacherini Stefano, Becheri Carlo, Grasso Daniele, Magnani Gianluca, Tempestini Matteo, Zingoni Niccolò, Ferrari Alessio
This paper offers a retrospective on collaborative projects that involved Alessandro Fantechi and the authors over the past two decades, from the shared perspective of the Formal Methods and Tools (FMT) lab of the Italian National Research Council (CNR) and former collaborators at General Electric (GE) Transportation and Alstom. The focus is on research and technology transfer efforts in the field of formal methods for railway systems, where Alessandro Fantechi’s contributions have been central to the development and application of formal specification, model-based verification, and tool-supported analysis. Joint work in projects such as ASTRail, 4SECURail, and TRACE-IT, as well as in industrial collaborations with Alstom and GE Transportation Systems illustrates the sustained impact of these activities on both academic research and industrial practice. This contribution reflects on the evolution of these efforts, the formal methods adopted, and the outcomes achieved in terms of methodologies, tools, and integration into safety-critical development processes. It also highlights the collaborative environment fostered across institutions and organizations, which has been instrumental in advancing the use of formal methods in the railway domain.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. 31-54
DOI: 10.1007/978-3-032-12484-5_3
Project(s): ADVancEd iNtegraTed evalUation of Railway systEms, Sustainable Mobility National Research Center
Metrics:
Basile Davide, Ter Beek Maurice Henri, Broccia Giovanna, Gnesi Stefania, Mazzanti Franco, Spagnolo Giorgio Oronzo, Bacherini Stefano, Becheri Carlo, Grasso Daniele, Magnani Gianluca, Tempestini Matteo, Zingoni Niccolò, Ferrari Alessio
This paper offers a retrospective on collaborative projects that involved Alessandro Fantechi and the authors over the past two decades, from the shared perspective of the Formal Methods and Tools (FMT) lab of the Italian National Research Council (CNR) and former collaborators at General Electric (GE) Transportation and Alstom. The focus is on research and technology transfer efforts in the field of formal methods for railway systems, where Alessandro Fantechi’s contributions have been central to the development and application of formal specification, model-based verification, and tool-supported analysis. Joint work in projects such as ASTRail, 4SECURail, and TRACE-IT, as well as in industrial collaborations with Alstom and GE Transportation Systems illustrates the sustained impact of these activities on both academic research and industrial practice. This contribution reflects on the evolution of these efforts, the formal methods adopted, and the outcomes achieved in terms of methodologies, tools, and integration into safety-critical development processes. It also highlights the collaborative environment fostered across institutions and organizations, which has been instrumental in advancing the use of formal methods in the railway domain.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. 31-54
DOI: 10.1007/978-3-032-12484-5_3
Project(s): ADVancEd iNtegraTed evalUation of Railway systEms, Sustainable Mobility National Research Center
Metrics:
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CNR IRIS 
| link.springer.com | CNR IRIS 
| CNR IRIS
2026
Journal article
Open Access
A history of formal methods in railways
Ter Beek Maurice, Fantechi Alessandro, Ferrari Alessio, Gnesi Stefania, Haxthausen Anne E., Lecomte Thierry
The engineering of industrial systems, particularly in safety-critical domains such as railways, demands rigorous verification and validation processes to ensure system dependability. Formal methods have emerged as powerful tools to complement traditional software engineering practices. In the railway sector, which increasingly relies on complex, distributed, and cyber-physical control systems, formal methods have demonstrated particular value for many decades now. In this paper, we provide a retrospective overview of the application of formal methods and tools in the railway domain, with emphasis on two prominent verification approaches and one frequently verified railway system: modeling and validation with the B method and tools and formal verification of interlocking systems by model checking. We explore their role in the design and development of key railway systems, highlighting both academic research and industrial success stories, as witnessed by international projects and initiatives. We conclude with an outlook on the potential of integrating AI and formal methods to enhance the efficiency of next-generation railway systems.Source: FORMAL ASPECTS OF COMPUTING
DOI: 10.1145/3802545
Project(s): ADVancEd iNtegraTed evalUation of Railway systEms, Sustainable Mobility National Research Center
Metrics:
Ter Beek Maurice, Fantechi Alessandro, Ferrari Alessio, Gnesi Stefania, Haxthausen Anne E., Lecomte Thierry
The engineering of industrial systems, particularly in safety-critical domains such as railways, demands rigorous verification and validation processes to ensure system dependability. Formal methods have emerged as powerful tools to complement traditional software engineering practices. In the railway sector, which increasingly relies on complex, distributed, and cyber-physical control systems, formal methods have demonstrated particular value for many decades now. In this paper, we provide a retrospective overview of the application of formal methods and tools in the railway domain, with emphasis on two prominent verification approaches and one frequently verified railway system: modeling and validation with the B method and tools and formal verification of interlocking systems by model checking. We explore their role in the design and development of key railway systems, highlighting both academic research and industrial success stories, as witnessed by international projects and initiatives. We conclude with an outlook on the potential of integrating AI and formal methods to enhance the efficiency of next-generation railway systems.Source: FORMAL ASPECTS OF COMPUTING
DOI: 10.1145/3802545
Project(s): ADVancEd iNtegraTed evalUation of Railway systEms, Sustainable Mobility National Research Center
Metrics:
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dl.acm.org | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Comparing soil organic carbon stock estimation methods for agricultural stony soils in Ireland: a regression-based approach
Bondi Giulia, Ferrari Alessio, Daly Karen, Schillaci Calogero, Lanigan Gary, Fenton Owen
Despite the importance of national-scale soil organic carbon (SOC) monitoring, current measurement approaches often lack reliability. Research has primarily focused on global standardisation, but there remains a critical need for methods that account for soil heterogeneity while ensuring logistical feasibility for large-scale monitoring. This study addresses this gap by comparing two SOC stock assessment methods, Equivalent Soil Mass (ESM) and Fixed Depth (FD), using 1184 soil samples spatially distributed in the southeast of Ireland. A linear regression model incorporating stoniness and depth was developed to improve ESM-based estimations. Results indicate that ESM, when adjusted for these factors, provides a reliable estimate of SOC stocks and can substitute FD under most conditions. Given its ease of implementation compared to FD and its ability to capture landscape variability, ESM is recommended for large-scale SOC monitoring. However, a hybrid approach may be needed in soils with extreme stoniness. These findings support integrating ESM into national carbon monitoring frameworks to enhance the accuracy of national SOC inventories.Source: SOIL USE AND MANAGEMENT, vol. 42 (issue 1)
DOI: 10.1111/sum.70180
Metrics:
Bondi Giulia, Ferrari Alessio, Daly Karen, Schillaci Calogero, Lanigan Gary, Fenton Owen
Despite the importance of national-scale soil organic carbon (SOC) monitoring, current measurement approaches often lack reliability. Research has primarily focused on global standardisation, but there remains a critical need for methods that account for soil heterogeneity while ensuring logistical feasibility for large-scale monitoring. This study addresses this gap by comparing two SOC stock assessment methods, Equivalent Soil Mass (ESM) and Fixed Depth (FD), using 1184 soil samples spatially distributed in the southeast of Ireland. A linear regression model incorporating stoniness and depth was developed to improve ESM-based estimations. Results indicate that ESM, when adjusted for these factors, provides a reliable estimate of SOC stocks and can substitute FD under most conditions. Given its ease of implementation compared to FD and its ability to capture landscape variability, ESM is recommended for large-scale SOC monitoring. However, a hybrid approach may be needed in soils with extreme stoniness. These findings support integrating ESM into national carbon monitoring frameworks to enhance the accuracy of national SOC inventories.Source: SOIL USE AND MANAGEMENT, vol. 42 (issue 1)
DOI: 10.1111/sum.70180
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bsssjournals.onlinelibrary.wiley.com | 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
Book
Open Access
Foreword to the 2nd Workshop on multi-disciplinary, open, and integrated requirements engineering (MO2RE’25)
Abualhaija S., Amyot D., Arora C., Ferrari A., Fucci D., Spoletini P.
The 2nd International Workshop on Multi-disciplinary, Open, and IntegRatEd RE (MO2RE) aims at highlighting the multiple facets of RE, clarifying its role within the software development process, and bringing together the broader SE community where RE is involved, e.g., RE and testing. MO2RE is envisioned as a shared place to gather the SE community around RE as a central topic.DOI: 10.1109/mo2re66661.2025.00005
Metrics:
Abualhaija S., Amyot D., Arora C., Ferrari A., Fucci D., Spoletini P.
The 2nd International Workshop on Multi-disciplinary, Open, and IntegRatEd RE (MO2RE) aims at highlighting the multiple facets of RE, clarifying its role within the software development process, and bringing together the broader SE community where RE is involved, e.g., RE and testing. MO2RE is envisioned as a shared place to gather the SE community around RE as a central topic.DOI: 10.1109/mo2re66661.2025.00005
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS
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
Contribution to book
Restricted
Handbook on natural language processing for requirements engineering: overview
Ferrari A., Ginde G.
Natural language processing (NLP) has played an important role in several computer science areas, and requirements engineering (RE) is not an exception. For more than 20 years, several works have been published on the application of NLP techniques to address RE-specific tasks, such as traceability, classification, defect detection and more. In recent years, the advent of massive and heterogeneous natural language (NL) RE-relevant sources, like tweets and app reviews, has increased the interest of the RE community in NLP. Furthermore, we witness a novel golden age of NLP technologies, enabled first by transformers, such as BERT, and more recently by large language models (LLMs), such as the GPT series and the Llama family. These developments offer the opportunity to solve long-standing RE tasks. Moreover, industrial case studies on NLP applications to RE problems also show that available NLP technologies are becoming increasingly industry ready. This book targets researchers and practitioners with a background in software engineering or information systems and presents a complete overview of the settled knowledge on NLP for RE. Its objective is to serve as a reference handbook for anyone interested in starting a journey in this field. This chapter introduces the book and provides an overview of its structure to guide the reader through the chapters.DOI: 10.1007/978-3-031-73143-3_1
Metrics:
Ferrari A., Ginde G.
Natural language processing (NLP) has played an important role in several computer science areas, and requirements engineering (RE) is not an exception. For more than 20 years, several works have been published on the application of NLP techniques to address RE-specific tasks, such as traceability, classification, defect detection and more. In recent years, the advent of massive and heterogeneous natural language (NL) RE-relevant sources, like tweets and app reviews, has increased the interest of the RE community in NLP. Furthermore, we witness a novel golden age of NLP technologies, enabled first by transformers, such as BERT, and more recently by large language models (LLMs), such as the GPT series and the Llama family. These developments offer the opportunity to solve long-standing RE tasks. Moreover, industrial case studies on NLP applications to RE problems also show that available NLP technologies are becoming increasingly industry ready. This book targets researchers and practitioners with a background in software engineering or information systems and presents a complete overview of the settled knowledge on NLP for RE. Its objective is to serve as a reference handbook for anyone interested in starting a journey in this field. This chapter introduces the book and provides an overview of its structure to guide the reader through the chapters.DOI: 10.1007/978-3-031-73143-3_1
Metrics:
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CNR IRIS | CNR IRIS | link.springer.com
2025
Conference article
Restricted
Automatic prompt engineering: the case of requirements classification
Zadenoori M. A., Zhao L., Alhoshan W., Ferrari A.
Context and motivation: Large language models (LLMs) are increasingly used to address requirements engineering (RE) tasks, including trace-link recovery, legal compliance, model generation, and others. Question/problem: Most of the existing studies rely on static, non-adaptive prompting strategies that do not fully harness the models’ capabilities. Specifically, these studies overlook the potential of automatic prompting engineering (APE), a technique that allows LLMs to self-generate and fine-tune prompts to improve task performance. Principal ideas/results: This research preview aims to study the effectiveness of APE techniques in LLM-powered RE tasks. As a preliminary step, we perform a benchmarking study in which we compare APE techniques with more traditional prompting solutions for the task of requirements classification. Our results show that, on average and with some exceptions, APE outperforms the baselines. We outline research avenues, including the evaluation and tailoring of APE for other RE tasks, and considering the human-in-the-loop. Contribution: To the best of our knowledge, this is the first study to introduce APE in RE, paving the way for a deeper exploration of LLMs’ potential in this field.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15588, pp. 217-225. Barcelona, Spain, 7 April 2025
DOI: 10.1007/978-3-031-88531-0_15
Metrics:
Zadenoori M. A., Zhao L., Alhoshan W., Ferrari A.
Context and motivation: Large language models (LLMs) are increasingly used to address requirements engineering (RE) tasks, including trace-link recovery, legal compliance, model generation, and others. Question/problem: Most of the existing studies rely on static, non-adaptive prompting strategies that do not fully harness the models’ capabilities. Specifically, these studies overlook the potential of automatic prompting engineering (APE), a technique that allows LLMs to self-generate and fine-tune prompts to improve task performance. Principal ideas/results: This research preview aims to study the effectiveness of APE techniques in LLM-powered RE tasks. As a preliminary step, we perform a benchmarking study in which we compare APE techniques with more traditional prompting solutions for the task of requirements classification. Our results show that, on average and with some exceptions, APE outperforms the baselines. We outline research avenues, including the evaluation and tailoring of APE for other RE tasks, and considering the human-in-the-loop. Contribution: To the best of our knowledge, this is the first study to introduce APE in RE, paving the way for a deeper exploration of LLMs’ potential in this field.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15588, pp. 217-225. Barcelona, Spain, 7 April 2025
DOI: 10.1007/978-3-031-88531-0_15
Metrics:
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CNR IRIS | CNR IRIS | link.springer.com
2025
Journal article
Open Access
Model transformation and property preservation in rigorous software development: a systematic literature review
Jadoon G., Ter Beek M. H., Ferrari A.
Rigorous software development involves using highly structured methods and processes in software and system engineering to ensure that the developed products are correct, reliable, and robust. In this context, model-driven development (MDD) has emerged as a development paradigm that emphasizes designing software systems by means of graphical or textual models at different levels of abstraction, which capture different aspects or dimensions of the system-to-be. At the core of MDD is model transformation, which is the process of translating one model into another, according to specific rules. Property preservation in MDD refers to maintaining specific properties of the system model during transformations, including structural, behavioral, and domain-specific constraints. Over the past decades, research on model transformation and property preservation has seen several contributions. In this paper, we present a systematic literature review (SLR) to compile information on study demographics, model properties considered, techniques to ensure property preservation, and other aspects. In addition, through thematic analysis, we highlight significant challenges and benefits associated with model transformation and property preservation. We analyze 182 research studies published between 2000 and 2024. Most of the studies concern case studies (52) and rigorous analysis (47), while experimental studies using human subjects are limited (1). Formal logic is the most commonly used transformation language, used in 35 studies, while the Unified Modeling Language (UML) is also used for source (55) and target (21) modeling. A total of 93 of the studies performed system testing on models, while 44 of the studies used transformation rules to verify transformation properties. Among the verified model properties, 64 studies focused on consistency management, while 4 are related to model maintainability and reuse. We conclude from our SLR that property preservation could be improved by using model-specific verification methods and strategies based on the considered model artifacts. Our research also provides a relevant contribution by identifying the major challenges in MDD and proposing relevant solutions.Source: THE JOURNAL OF SYSTEMS AND SOFTWARE, vol. 230
DOI: 10.1016/j.jss.2025.112508
Metrics:
Jadoon G., Ter Beek M. H., Ferrari A.
Rigorous software development involves using highly structured methods and processes in software and system engineering to ensure that the developed products are correct, reliable, and robust. In this context, model-driven development (MDD) has emerged as a development paradigm that emphasizes designing software systems by means of graphical or textual models at different levels of abstraction, which capture different aspects or dimensions of the system-to-be. At the core of MDD is model transformation, which is the process of translating one model into another, according to specific rules. Property preservation in MDD refers to maintaining specific properties of the system model during transformations, including structural, behavioral, and domain-specific constraints. Over the past decades, research on model transformation and property preservation has seen several contributions. In this paper, we present a systematic literature review (SLR) to compile information on study demographics, model properties considered, techniques to ensure property preservation, and other aspects. In addition, through thematic analysis, we highlight significant challenges and benefits associated with model transformation and property preservation. We analyze 182 research studies published between 2000 and 2024. Most of the studies concern case studies (52) and rigorous analysis (47), while experimental studies using human subjects are limited (1). Formal logic is the most commonly used transformation language, used in 35 studies, while the Unified Modeling Language (UML) is also used for source (55) and target (21) modeling. A total of 93 of the studies performed system testing on models, while 44 of the studies used transformation rules to verify transformation properties. Among the verified model properties, 64 studies focused on consistency management, while 4 are related to model maintainability and reuse. We conclude from our SLR that property preservation could be improved by using model-specific verification methods and strategies based on the considered model artifacts. Our research also provides a relevant contribution by identifying the major challenges in MDD and proposing relevant solutions.Source: THE JOURNAL OF SYSTEMS AND SOFTWARE, vol. 230
DOI: 10.1016/j.jss.2025.112508
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CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Other
Open Access
HumAInFlow : a no-code platform for modelling and simulating Human-AI workflows
Broccia G., Cirillo R., Ferrari A., Lelii L., Spagnolo G. O.
The increasing integration of Generative AI (GenAI) agents into socio-technical systems, calls for platforms that can model, simulate, and analyse workflows involving both automated and human tasks. Existing agentic AI frameworks largely focus on automation and remain tightly bound to specific SDKs, often lacking structured support for human-in-the-loop modelling and simulation. To address these limitations, we introduce HumAInFlow, a no-code platform for modelling and simulating socio-technical workflows that explicitly integrates human roles as first-class nodes and supports their simulation via large language models (LLMs). The platform is SDK-agnostic, supports both local and remote LLM execution, and integrates the Model Context Protocol (MCP), ensuring interoperability and extensibility. Comparative analysis shows that HumAInFlow advances the state of the art by combining privacy-preserving deployment, execution monitoring, reproducibility, and explicit support for human–AI collaboration.DOI: 10.32079/isti-tr-2025/011
Metrics:
Broccia G., Cirillo R., Ferrari A., Lelii L., Spagnolo G. O.
The increasing integration of Generative AI (GenAI) agents into socio-technical systems, calls for platforms that can model, simulate, and analyse workflows involving both automated and human tasks. Existing agentic AI frameworks largely focus on automation and remain tightly bound to specific SDKs, often lacking structured support for human-in-the-loop modelling and simulation. To address these limitations, we introduce HumAInFlow, a no-code platform for modelling and simulating socio-technical workflows that explicitly integrates human roles as first-class nodes and supports their simulation via large language models (LLMs). The platform is SDK-agnostic, supports both local and remote LLM execution, and integrates the Model Context Protocol (MCP), ensuring interoperability and extensibility. Comparative analysis shows that HumAInFlow advances the state of the art by combining privacy-preserving deployment, execution monitoring, reproducibility, and explicit support for human–AI collaboration.DOI: 10.32079/isti-tr-2025/011
Metrics:
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2025
Conference article
Open Access
An experience report on leveraging LLMs for GUI generation: automating coding to prioritise creativity
Broccia G., Borselli A., Cefaloni M. R., Delcorno F., Ferrari A.
The design of graphical user interfaces (GUIs) is a complex and time-consuming process that begins with identifying user roles and gathering requirements through interviews, surveys, or workshops. Designers then create low-fidelity sketches or digital wireframes, organising information into logical sections and selecting visual elements to enhance usability. This iterative process often demands extensive refinement based on stakeholder feedback, making mockup creation—especially for interactive prototypes—a time-consuming task. In particular, the mockup development process often entails spending significant effort on clerical activities, such as programming and debugging tasks, rather than concentrating on creativity, human interaction, and quick feedback cycles with stakeholders. This paper investigates whether large language models (LLMs) can assist GUI designers in streamlining the design process—reducing time and effort while maintaining design quality—enabling them to focus on the human aspects of creativity and user interaction by offloading technical programming tasks to the machine. We document our experience designing a dashboard for predictive maintenance in railways, illustrating how LLMs can support key tasks such as requirement analysis, information organisation, and mockup generation and refinement. We discuss insights and lessons learned, including the importance of clear requirements, the impact of LLM choice, and the benefits of iterative refinement in achieving stakeholder alignment. Our study shows that LLMs can support the GUI design process by automating specific tasks, thereby reducing design effort and enhancing the overall quality and satisfaction of the final product.
Broccia G., Borselli A., Cefaloni M. R., Delcorno F., Ferrari A.
The design of graphical user interfaces (GUIs) is a complex and time-consuming process that begins with identifying user roles and gathering requirements through interviews, surveys, or workshops. Designers then create low-fidelity sketches or digital wireframes, organising information into logical sections and selecting visual elements to enhance usability. This iterative process often demands extensive refinement based on stakeholder feedback, making mockup creation—especially for interactive prototypes—a time-consuming task. In particular, the mockup development process often entails spending significant effort on clerical activities, such as programming and debugging tasks, rather than concentrating on creativity, human interaction, and quick feedback cycles with stakeholders. This paper investigates whether large language models (LLMs) can assist GUI designers in streamlining the design process—reducing time and effort while maintaining design quality—enabling them to focus on the human aspects of creativity and user interaction by offloading technical programming tasks to the machine. We document our experience designing a dashboard for predictive maintenance in railways, illustrating how LLMs can support key tasks such as requirement analysis, information organisation, and mockup generation and refinement. We discuss insights and lessons learned, including the importance of clear requirements, the impact of LLM choice, and the benefits of iterative refinement in achieving stakeholder alignment. Our study shows that LLMs can support the GUI design process by automating specific tasks, thereby reducing design effort and enhancing the overall quality and satisfaction of the final product.
See at:
creare.iese.de | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Data mining in railway diagnostic data for predictive maintenance
Millitarì G., Ferrari A., Spagnolo G. O.
We describe the initial and crucial phase of an analysis for a project belonging to the Spoke 4 on “Railway Transportation” of the Italian National Center for Sustainable Mobility (MOST), which is part of the National Recovery and Resilience Plan (PNRR). The objective of the project is the implementation of a predictive maintenance strategy within the decision-making process of Trenord.Source: ERCIM NEWS, vol. 140, pp. 14-15
Project(s): Spoke 4 on “Railway Transportation” of the Italian National Center for Sustainable Mobility (MOST)
Millitarì G., Ferrari A., Spagnolo G. O.
We describe the initial and crucial phase of an analysis for a project belonging to the Spoke 4 on “Railway Transportation” of the Italian National Center for Sustainable Mobility (MOST), which is part of the National Recovery and Resilience Plan (PNRR). The objective of the project is the implementation of a predictive maintenance strategy within the decision-making process of Trenord.Source: ERCIM NEWS, vol. 140, pp. 14-15
Project(s): Spoke 4 on “Railway Transportation” of the Italian National Center for Sustainable Mobility (MOST)
See at:
ercim-news.ercim.eu | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Formal requirements engineering and large language models: a two-way roadmap
Ferrari A., Spoletini P.
Context: Large Language Models (LLMs) have made remarkable advancements in emulating human linguistic capabilities, showing potential also in executing various requirements engineering (RE) tasks. However, despite their generally good performance, the adoption of LLM-generated solutions and artefacts prompts concerns about their correctness, fairness, and trustworthiness. Objective: This paper aims to address the concerns associated with the use of LLMs in RE activities. Specifically, it seeks to develop a roadmap that leverages formal methods (FMs) to provide guarantees of correctness, fairness, and trustworthiness when LLMs are utilised in RE. Symmetrically, it aims to explore how LLMs can be employed to make FMs more accessible. Methods: We use two sets of examples to show the current limits of FMs when used in software development and of LLMs when used for RE tasks. The highlighted limitations are addressed by proposing two roadmaps grounded in the current literature and technologies. Results: The proposed examples show the potential and limits of FMs in supporting software development and of LLMs when used for RE tasks. The initial investigation into how these limitations can be overcome has been concretised in two detailed roadmaps for the RE and, more largely, the software engineering community. Conclusion: The proposed roadmaps offer a promising approach to address the concerns of correctness, fairness, and trustworthiness associated with the use of LLMs in RE tasks through the use of FMs and to enhance the accessibility of FMs by utilising LLMs.Source: INFORMATION AND SOFTWARE TECHNOLOGY, vol. 181 (issue 107697)
DOI: 10.1016/j.infsof.2025.107697
Metrics:
Ferrari A., Spoletini P.
Context: Large Language Models (LLMs) have made remarkable advancements in emulating human linguistic capabilities, showing potential also in executing various requirements engineering (RE) tasks. However, despite their generally good performance, the adoption of LLM-generated solutions and artefacts prompts concerns about their correctness, fairness, and trustworthiness. Objective: This paper aims to address the concerns associated with the use of LLMs in RE activities. Specifically, it seeks to develop a roadmap that leverages formal methods (FMs) to provide guarantees of correctness, fairness, and trustworthiness when LLMs are utilised in RE. Symmetrically, it aims to explore how LLMs can be employed to make FMs more accessible. Methods: We use two sets of examples to show the current limits of FMs when used in software development and of LLMs when used for RE tasks. The highlighted limitations are addressed by proposing two roadmaps grounded in the current literature and technologies. Results: The proposed examples show the potential and limits of FMs in supporting software development and of LLMs when used for RE tasks. The initial investigation into how these limitations can be overcome has been concretised in two detailed roadmaps for the RE and, more largely, the software engineering community. Conclusion: The proposed roadmaps offer a promising approach to address the concerns of correctness, fairness, and trustworthiness associated with the use of LLMs in RE tasks through the use of FMs and to enhance the accessibility of FMs by utilising LLMs.Source: INFORMATION AND SOFTWARE TECHNOLOGY, vol. 181 (issue 107697)
DOI: 10.1016/j.infsof.2025.107697
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Book
Open Access
Preface to the Handbook on natural language processing for requirements engineering
Ferrari A., Ginde G.
This handbook provides a comprehensive guide on how natural language processing (NLP) can be leveraged to enhance various aspects of requirements engineering (RE), leading the reader from the exploration of fundamental concepts and techniques to the practical implementation of NLP for RE solutions in real-world scenarios. The book features contributions from researchers with both academic and industrial experience. It is organized into three parts, each focusing on different aspects of applying NLP to RE: Part I – NLP for Downstream RE Tasks delves into the application of NLP techniques to tasks that are typically part of the RE process. It includes chapters on NLP for requirements classification, requirements similarity and retrieval, requirements traceability, defect detection, and automated terminology and relations extraction. Next, Part II – NLP for Specialised Types of Requirements and Artefacts explores how NLP can be tailored to handle specific requirement types and artefacts. The chapters cover legal requirements processing, privacy requirements acquisition and analysis, user feedback intelligence, mining issue trackers, and analysis of user story requirements. Eventually, Part III – NLP for RE in Practice addresses practical applications and tools for implementing NLP in RE. It includes a chapter on the different tools that use NLP techniques for RE tasks, followed by chapters on empirical evaluation of tools, practical guidelines for selecting and evaluating NLP techniques, guidelines on using large language models (LLMs) in RE, and dealing with data challenges in RE. The book is designed for a diverse audience, including Ph.D. students, researchers, and practitioners. Ph.D. students can benefit from a comprehensive guide to the topic of NLP for RE and acquire the essential background for their studies. Researchers can identify further triggers for scientific exploration, based on the currently settled knowledge in the field. Eventually, practitioners facing challenges with NL requirements can find practical insights to enhance their RE processes using NLP.DOI: 10.1007/978-3-031-73143-3
Metrics:
Ferrari A., Ginde G.
This handbook provides a comprehensive guide on how natural language processing (NLP) can be leveraged to enhance various aspects of requirements engineering (RE), leading the reader from the exploration of fundamental concepts and techniques to the practical implementation of NLP for RE solutions in real-world scenarios. The book features contributions from researchers with both academic and industrial experience. It is organized into three parts, each focusing on different aspects of applying NLP to RE: Part I – NLP for Downstream RE Tasks delves into the application of NLP techniques to tasks that are typically part of the RE process. It includes chapters on NLP for requirements classification, requirements similarity and retrieval, requirements traceability, defect detection, and automated terminology and relations extraction. Next, Part II – NLP for Specialised Types of Requirements and Artefacts explores how NLP can be tailored to handle specific requirement types and artefacts. The chapters cover legal requirements processing, privacy requirements acquisition and analysis, user feedback intelligence, mining issue trackers, and analysis of user story requirements. Eventually, Part III – NLP for RE in Practice addresses practical applications and tools for implementing NLP in RE. It includes a chapter on the different tools that use NLP techniques for RE tasks, followed by chapters on empirical evaluation of tools, practical guidelines for selecting and evaluating NLP techniques, guidelines on using large language models (LLMs) in RE, and dealing with data challenges in RE. The book is designed for a diverse audience, including Ph.D. students, researchers, and practitioners. Ph.D. students can benefit from a comprehensive guide to the topic of NLP for RE and acquire the essential background for their studies. Researchers can identify further triggers for scientific exploration, based on the currently settled knowledge in the field. Eventually, practitioners facing challenges with NL requirements can find practical insights to enhance their RE processes using NLP.DOI: 10.1007/978-3-031-73143-3
Metrics:
See at:
CNR IRIS | link.springer.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
Conference article
Open Access
LLM-Guided indoor navigation with multimodal map understanding
Coffrini A., Barsocchi P., Furfari F., Crivello A., Ferrari A.
Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.DOI: 10.1109/ipin66788.2025.11212934
DOI: 10.48550/arxiv.2503.11702
Metrics:
Coffrini A., Barsocchi P., Furfari F., Crivello A., Ferrari A.
Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.DOI: 10.1109/ipin66788.2025.11212934
DOI: 10.48550/arxiv.2503.11702
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | ieeexplore.ieee.org | doi.org | doi.org | CNR IRIS | CNR IRIS
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
2025
Conference article
Open Access
On the impact of requirements smells in prompts: the case of automated traceability
Vogelsang A., Korn A., Broccia G., Ferrari A., Fischbach J., Arora C.
Large language models (LLMs) are increasingly used to generate software artifacts, such as source code, tests, and trace links. Requirements play a central role in shaping the input prompts that guide LLMs, as they are often used as part of the prompts to synthesize the artifacts. However, the impact of requirements formulation on LLM performance remains unclear. In this paper, we investigate the role of requirements smells— indicators of potential issues like ambiguity and inconsistency— when used in prompts for LLMs. We conducted experiments using two LLMs focusing on automated trace link generation between requirements and code. Our results show mixed outcomes: while requirements smells had a small but significant effect when predicting whether a requirement was implemented in a piece of code (i.e., a trace link exists), no significant effect was observed when tracing the requirements with the associated lines of code. These findings suggest that requirements smells can affect LLM performance in certain SE tasks but may not uniformly impact all tasks. We highlight the need for further research to understand these nuances and propose future work toward developing guidelines for mitigating the negative effects of requirements smells in AI-driven SE processes.Source: PROCEEDINGS - INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, pp. 51-55. Ottawa, Canada, 27/04-03/05/2025
DOI: 10.1109/icse-nier66352.2025.00016
Metrics:
Vogelsang A., Korn A., Broccia G., Ferrari A., Fischbach J., Arora C.
Large language models (LLMs) are increasingly used to generate software artifacts, such as source code, tests, and trace links. Requirements play a central role in shaping the input prompts that guide LLMs, as they are often used as part of the prompts to synthesize the artifacts. However, the impact of requirements formulation on LLM performance remains unclear. In this paper, we investigate the role of requirements smells— indicators of potential issues like ambiguity and inconsistency— when used in prompts for LLMs. We conducted experiments using two LLMs focusing on automated trace link generation between requirements and code. Our results show mixed outcomes: while requirements smells had a small but significant effect when predicting whether a requirement was implemented in a piece of code (i.e., a trace link exists), no significant effect was observed when tracing the requirements with the associated lines of code. These findings suggest that requirements smells can affect LLM performance in certain SE tasks but may not uniformly impact all tasks. We highlight the need for further research to understand these nuances and propose future work toward developing guidelines for mitigating the negative effects of requirements smells in AI-driven SE processes.Source: PROCEEDINGS - INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, pp. 51-55. Ottawa, Canada, 27/04-03/05/2025
DOI: 10.1109/icse-nier66352.2025.00016
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS