2023
Conference article
Open Access
Strategies, benefits and challenges of app store-inspired requirements elicitation
Ferrari A., Spoletini P.
App store-inspired elicitation is the practice of exploring competitors' apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages and possible issues. This paper aims to empirically analyse this technique in a realistic scenario, in which it is used to extend the requirements of a product that were initially captured by means of more traditional requirements elicitation interviews. Considering this scenario, we conduct an experimental simulation with 58 analysts and collect qualitative data. We perform thematic analysis of the data to identify strategies, benefits, and challenges of app store-inspired elicitation, as well as differences with respect to interviews in the considered elicitation setting. Our results show that: (1) specific guidelines and procedures are required to better conduct app store-inspired elicitation; (2) current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and evaluation of competing products; (3) while interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges and suggests possible benefits to fostering app store-inspired elicitation among practitioners.Source: ICSE 2023 - 45th International Conference on Software Engineering, pp. 1290–1302, Melbourne, Australia, 14-20/05/2023
DOI: 10.1109/icse48619.2023.00114
Metrics:
Ferrari A., Spoletini P.
App store-inspired elicitation is the practice of exploring competitors' apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages and possible issues. This paper aims to empirically analyse this technique in a realistic scenario, in which it is used to extend the requirements of a product that were initially captured by means of more traditional requirements elicitation interviews. Considering this scenario, we conduct an experimental simulation with 58 analysts and collect qualitative data. We perform thematic analysis of the data to identify strategies, benefits, and challenges of app store-inspired elicitation, as well as differences with respect to interviews in the considered elicitation setting. Our results show that: (1) specific guidelines and procedures are required to better conduct app store-inspired elicitation; (2) current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and evaluation of competing products; (3) while interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges and suggests possible benefits to fostering app store-inspired elicitation among practitioners.Source: ICSE 2023 - 45th International Conference on Software Engineering, pp. 1290–1302, Melbourne, Australia, 14-20/05/2023
DOI: 10.1109/icse48619.2023.00114
Metrics:
See at:
ISTI Repository 
| dl.acm.org 
| CNR ExploRA
2023
Conference article
Open Access
Remote sensing and machine learning for riparian vegetation detection and classification
Fiorentini N., Bacco M., Ferrari A., Rovai M., Brunori G.
Precise and reliable identification of riparian vegetation along rivers is of paramount importance for managing bodies, enabling them to accurately plan key duties, such as the design of river maintenance interventions. Nonetheless, manual mapping is significantly expensive in terms of time and human costs, especially when authorities have to manage extensive river networks. Accordingly, in the present paper, we propose a methodology for classifying and automatically mapping the riparian vegetation of urban rivers. Specifically, the calibration of an unsupervised (Isodata Clustering) and a supervised (Random Forest) machine learning algorithm (MLA) is carried out for the classification of the riparian vegetation detected in high-resolution (1m) aerial orthoimages. Riparian vegetation is classified using Normalized Difference Vegetation Index (NDVI) features. In the framework of this research, the Isodata Clustering slightly outperforms the Random Forest, achieving a higher level of predictive performance and reliability throughout all the computed performance metrics. Moreover, being unsupervised, it does not require ground truth information, which makes it particularly competitive in terms of annotation costs when compared with supervised algorithms, and definitely appropriate in case of limited resources. We encourage river authorities to use MLA-based tools, such as the ones we propose in this work, for mapping riparian vegetation, since they can bring relevant benefits, such as limited implementation costs, easy calibration, fast training, and adequate reliability.Source: MetroAgriFor 2023 - IEEE International Workshop on Metrology for Agriculture and Forestry, Pisa, Italy, 6-8/11/2023
Project(s): DESIRA
Fiorentini N., Bacco M., Ferrari A., Rovai M., Brunori G.
Precise and reliable identification of riparian vegetation along rivers is of paramount importance for managing bodies, enabling them to accurately plan key duties, such as the design of river maintenance interventions. Nonetheless, manual mapping is significantly expensive in terms of time and human costs, especially when authorities have to manage extensive river networks. Accordingly, in the present paper, we propose a methodology for classifying and automatically mapping the riparian vegetation of urban rivers. Specifically, the calibration of an unsupervised (Isodata Clustering) and a supervised (Random Forest) machine learning algorithm (MLA) is carried out for the classification of the riparian vegetation detected in high-resolution (1m) aerial orthoimages. Riparian vegetation is classified using Normalized Difference Vegetation Index (NDVI) features. In the framework of this research, the Isodata Clustering slightly outperforms the Random Forest, achieving a higher level of predictive performance and reliability throughout all the computed performance metrics. Moreover, being unsupervised, it does not require ground truth information, which makes it particularly competitive in terms of annotation costs when compared with supervised algorithms, and definitely appropriate in case of limited resources. We encourage river authorities to use MLA-based tools, such as the ones we propose in this work, for mapping riparian vegetation, since they can bring relevant benefits, such as limited implementation costs, easy calibration, fast training, and adequate reliability.Source: MetroAgriFor 2023 - IEEE International Workshop on Metrology for Agriculture and Forestry, Pisa, Italy, 6-8/11/2023
Project(s): DESIRA
See at:
ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Zero-shot learning for requirements classification: an exploratory study
Alhoshan W., Ferrari A., Zhao L.
Context: Requirements engineering researchers have been experimenting with machine learning and deep learning approaches for a range of RE tasks, such as requirements classification, requirements tracing, ambiguity detection, and modelling. However, most of today's ML/DL approaches are based on supervised learning techniques, meaning that they need to be trained using a large amount of task-specific labelled training data. This constraint poses an enormous challenge to RE researchers, as the lack of labelled data makes it difficult for them to fully exploit the benefit of advanced ML/DL technologies. Objective: This paper addresses this problem by showing how a zero-shot learning approach can be used for requirements classification without using any labelled training data. We focus on the classification task because many RE tasks can be framed as classification problems. Method: The ZSL approach used in our study employs contextual word-embeddings and transformer-based language models. We demonstrate this approach through a series of experiments to perform three classification tasks: (1)FR/NFR: classification functional requirements vs non-functional requirements; (2)NFR: identification of NFR classes; (3)Security: classification of security vs non-security requirements. Results: The study shows that the ZSL approach achieves an F1 score of 0.66 for the FR/NFR task. For the NFR task, the approach yields F1~0.72-0.80, considering the most frequent classes. For the Security task, F1~0.66. All of the aforementioned F1 scores are achieved with zero-training efforts. Conclusion: This study demonstrates the potential of ZSL for requirements classification. An important implication is that it is possible to have very little or no training data to perform classification tasks. The proposed approach thus contributes to the solution of the long-standing problem of data shortage in RE.Source: Information and software technology 159 (2023). doi:10.1016/j.infsof.2023.107202
DOI: 10.1016/j.infsof.2023.107202
Metrics:
Alhoshan W., Ferrari A., Zhao L.
Context: Requirements engineering researchers have been experimenting with machine learning and deep learning approaches for a range of RE tasks, such as requirements classification, requirements tracing, ambiguity detection, and modelling. However, most of today's ML/DL approaches are based on supervised learning techniques, meaning that they need to be trained using a large amount of task-specific labelled training data. This constraint poses an enormous challenge to RE researchers, as the lack of labelled data makes it difficult for them to fully exploit the benefit of advanced ML/DL technologies. Objective: This paper addresses this problem by showing how a zero-shot learning approach can be used for requirements classification without using any labelled training data. We focus on the classification task because many RE tasks can be framed as classification problems. Method: The ZSL approach used in our study employs contextual word-embeddings and transformer-based language models. We demonstrate this approach through a series of experiments to perform three classification tasks: (1)FR/NFR: classification functional requirements vs non-functional requirements; (2)NFR: identification of NFR classes; (3)Security: classification of security vs non-security requirements. Results: The study shows that the ZSL approach achieves an F1 score of 0.66 for the FR/NFR task. For the NFR task, the approach yields F1~0.72-0.80, considering the most frequent classes. For the Security task, F1~0.66. All of the aforementioned F1 scores are achieved with zero-training efforts. Conclusion: This study demonstrates the potential of ZSL for requirements classification. An important implication is that it is possible to have very little or no training data to perform classification tasks. The proposed approach thus contributes to the solution of the long-standing problem of data shortage in RE.Source: Information and software technology 159 (2023). doi:10.1016/j.infsof.2023.107202
DOI: 10.1016/j.infsof.2023.107202
Metrics:
See at:
ISTI Repository | ISTI Repository | ISTI Repository | www.sciencedirect.com | CNR ExploRA
2023
Conference article
Unknown
Digitalising agriculture: design and development of a modelling web environment for end-users
Mannari C., Anichini E., Malizia A., Turchi T., Ferrari A., Bacco M.
Modern digital technologies have a promising potential in the development of sustainable agriculture. For example, cloud computers powered by 5G IoT components allow the development of sophisticated applications -- e.g., for food traceability, pest detection, automatic irrigation -- also making use of different AI-based techniques. At the same time, digitalisation in agriculture is considered a socio-technical process to be evaluated by different stakeholders through collaborative approaches. Model-based requirements engineering strategies, which leverage diagrammatic notations, can support information exchange between different domains. Based on the evaluation of different solutions, we found a lack of tools for designing and modelling systems accessible to end-users. Most professional tools are desktop applications based on complex interactions, limited user experience, or generalist web platforms needing more formal components. In our study, we developed a prototype of a web environment for modelling systems based on a visual language that can be exported into standard code. We aim to involve end-users in modelling their digital ecosystems as a preliminary task for developing further applications. The prototype was evaluated in a workshop with experts following the cognitive walkthrough methodology for usability inspection. The evaluation highlighted the UI requirements to support easy-to-understand visual elements and maximise the understanding of actions while limiting user errors.Source: VL/HCC 2023 - IEEE Symposium on Visual Languages and Human-Centric Computing, Washington, DC, USA, 2-6/10/2023
Mannari C., Anichini E., Malizia A., Turchi T., Ferrari A., Bacco M.
Modern digital technologies have a promising potential in the development of sustainable agriculture. For example, cloud computers powered by 5G IoT components allow the development of sophisticated applications -- e.g., for food traceability, pest detection, automatic irrigation -- also making use of different AI-based techniques. At the same time, digitalisation in agriculture is considered a socio-technical process to be evaluated by different stakeholders through collaborative approaches. Model-based requirements engineering strategies, which leverage diagrammatic notations, can support information exchange between different domains. Based on the evaluation of different solutions, we found a lack of tools for designing and modelling systems accessible to end-users. Most professional tools are desktop applications based on complex interactions, limited user experience, or generalist web platforms needing more formal components. In our study, we developed a prototype of a web environment for modelling systems based on a visual language that can be exported into standard code. We aim to involve end-users in modelling their digital ecosystems as a preliminary task for developing further applications. The prototype was evaluated in a workshop with experts following the cognitive walkthrough methodology for usability inspection. The evaluation highlighted the UI requirements to support easy-to-understand visual elements and maximise the understanding of actions while limiting user errors.Source: VL/HCC 2023 - IEEE Symposium on Visual Languages and Human-Centric Computing, Washington, DC, USA, 2-6/10/2023
See at: CNR ExploRA
2023
Conference article
Open Access
Evaluating a language workbench: from working memory capacity to comprehension to acceptance
Broccia G., Ferrari A., Ter Beek M. H., Cazzola W., Favalli L., Bertolotti F.
Language workbenches are tools that enable the definition, reuse and composition of programming languages and their ecosystem. This breed of frameworks aims to make the development of new languages easier and more affordable. Consequently, the comprehensibility of the language used in a language workbench (i.e., the meta-language) should be an important aspect to consider and evaluate. To the best of our knowledge, although the quantitative aspects of language workbenches are often discussed in the literature, the evaluation of comprehensibility is typically neglected. Neverlang is a language workbench that enables the definition of languages with a modular approach. This paper presents a preliminary study that intends to assess the comprehensibility of Neverlang programs, evaluated in terms of users' effectiveness and efficiency in a code comprehension task. The study also investigates the relationship between Neverlang comprehensibility and the users' working memory capacity. Furthermore, we intend to capture the relationship between Neverlang comprehensibility and users' acceptance, in terms of perceived ease of use, perceived usefulness, and intention to use. Our preliminary results on 10 subjects suggest that the users' working memory capacity may be related to the ability to comprehend Neverlang programs. On the other hand, effectiveness and efficiency do not appear to be associated with an increase in users' acceptance variables.Source: ICPC'23 - 31st IEEE/ACM International Conference on Program Comprehension, pp. 54–58, Melbourne, Australia, 15-16/05/2023
DOI: 10.1109/icpc58990.2023.00017
Metrics:
Broccia G., Ferrari A., Ter Beek M. H., Cazzola W., Favalli L., Bertolotti F.
Language workbenches are tools that enable the definition, reuse and composition of programming languages and their ecosystem. This breed of frameworks aims to make the development of new languages easier and more affordable. Consequently, the comprehensibility of the language used in a language workbench (i.e., the meta-language) should be an important aspect to consider and evaluate. To the best of our knowledge, although the quantitative aspects of language workbenches are often discussed in the literature, the evaluation of comprehensibility is typically neglected. Neverlang is a language workbench that enables the definition of languages with a modular approach. This paper presents a preliminary study that intends to assess the comprehensibility of Neverlang programs, evaluated in terms of users' effectiveness and efficiency in a code comprehension task. The study also investigates the relationship between Neverlang comprehensibility and the users' working memory capacity. Furthermore, we intend to capture the relationship between Neverlang comprehensibility and users' acceptance, in terms of perceived ease of use, perceived usefulness, and intention to use. Our preliminary results on 10 subjects suggest that the users' working memory capacity may be related to the ability to comprehend Neverlang programs. On the other hand, effectiveness and efficiency do not appear to be associated with an increase in users' acceptance variables.Source: ICPC'23 - 31st IEEE/ACM International Conference on Program Comprehension, pp. 54–58, Melbourne, Australia, 15-16/05/2023
DOI: 10.1109/icpc58990.2023.00017
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Report
Open Access
DESIRA - D3.5 Third set of practice abstracts
Bacco F. M., Ferrari A., Berg M., Schroth C., Rendl C., Marinos-Kouris C., Toli E., Koltsida P., Ortolani L., Lepore F., Townsend L., Hardy C., Fiorentini N., Brunori G.
This document provides DESIRA's third set of practice abstracts (PAs) which is a compilation of seven PAs. Those PAs are based on the experiences, lessons learned, project actions and reporting of the WP3 activities that aimed at the development of scenarios and showcasing of technologies building on the concept of digital game changers. Tasks 3.5 'Use Case development' and 3.6 'Showcase Technologies', are the main contributing project tasks that provided concrete results on which these seven PAs, cited in this report, are based. The first five PAs provide a concise description of five use cases that were developed during the second period of the DESIRA project. An array of conducted activities inside the boundaries of five preselected Living Labs, and with the participation of those LL's stakeholders, were planned so that WP3 could culminate in the development of five technology adoption use cases. The last two PAs of this report, supplement the five aforementioned use case PAs by showcasing two additional promising technology solutions that have the potential to contribute to sustainable digital transition pathways, as those are defined by the DESIRA's theoretical framework and as reflected by the examined agro-rural-forestry settings of this project. For a thorough and detailed analysis of the methodology, activities, and outcomes that contributed to the production of the use cases and showcase technology reports, it is recommended the reading of deliverables D3.3 'Use Cases Report' and D3.4 'Showcase Technology Report' which are the foundation documents on which this deliverable is based on.Source: ISTI Project Report, DESIRA, D3.5, pp.1–19, 2023
Project(s): DESIRA
Bacco F. M., Ferrari A., Berg M., Schroth C., Rendl C., Marinos-Kouris C., Toli E., Koltsida P., Ortolani L., Lepore F., Townsend L., Hardy C., Fiorentini N., Brunori G.
This document provides DESIRA's third set of practice abstracts (PAs) which is a compilation of seven PAs. Those PAs are based on the experiences, lessons learned, project actions and reporting of the WP3 activities that aimed at the development of scenarios and showcasing of technologies building on the concept of digital game changers. Tasks 3.5 'Use Case development' and 3.6 'Showcase Technologies', are the main contributing project tasks that provided concrete results on which these seven PAs, cited in this report, are based. The first five PAs provide a concise description of five use cases that were developed during the second period of the DESIRA project. An array of conducted activities inside the boundaries of five preselected Living Labs, and with the participation of those LL's stakeholders, were planned so that WP3 could culminate in the development of five technology adoption use cases. The last two PAs of this report, supplement the five aforementioned use case PAs by showcasing two additional promising technology solutions that have the potential to contribute to sustainable digital transition pathways, as those are defined by the DESIRA's theoretical framework and as reflected by the examined agro-rural-forestry settings of this project. For a thorough and detailed analysis of the methodology, activities, and outcomes that contributed to the production of the use cases and showcase technology reports, it is recommended the reading of deliverables D3.3 'Use Cases Report' and D3.4 'Showcase Technology Report' which are the foundation documents on which this deliverable is based on.Source: ISTI Project Report, DESIRA, D3.5, pp.1–19, 2023
Project(s): DESIRA
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
ModeLLer - a prototype to support requirements elicitation in co-design environments
Mannari C., Anichini E., Bacco M., Ferrari A., Turchi T., Malizia A.
This contribution presents ModeLLer, a prototype of a web tool for system modelling based on a block-based visual editor. The aim of ModeLLer is to enable collaborative environments in requirements elicitation, allowing end-users to create UML class diagrams without any knowledge of the (semi-)formal UML notation.Source: RE 2023 - 31st IEEE International Requirements Engineering Conference, Hannover, Germany, 04-08/09/2023
Mannari C., Anichini E., Bacco M., Ferrari A., Turchi T., Malizia A.
This contribution presents ModeLLer, a prototype of a web tool for system modelling based on a block-based visual editor. The aim of ModeLLer is to enable collaborative environments in requirements elicitation, allowing end-users to create UML class diagrams without any knowledge of the (semi-)formal UML notation.Source: RE 2023 - 31st IEEE International Requirements Engineering Conference, Hannover, Germany, 04-08/09/2023
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Experimenting with formal verification and model-based development in railways: the case of UMC and Sparx Enterprise Architect
Basile D., Mazzanti F., Ferrari A.
The use of formal methods can reduce the time and costs associated with railway signalling systems development and maintenance, and improve correct behaviour and safety. The integration of formal methods into industrial model-based development tools has been the subject of recent research, indicating the potential transfer of academic techniques to enhance industrial tools. This paper explores the integration of an academic formal verification tool, UML Model Checker (UMC), with an industrial model-based development tool, Sparx Enterprise Architect (Sparx EA). The case study being analyzed is a railway standard interface. The paper demonstrates how formal verification techniques from academic tools can be integrated into industrial development practices using industrial tools, and how simulation in Sparx EA can be derived from traces generated by the UMC formal verification activity. From this experience, we derive a set of lessons learned and research challenges.Source: FMICS 2023 - 28th International Conference on Formal Methods for Industrial Critical Systems, pp. 1–21, Antwerp, Belgium, 20-22/09/2023
DOI: 10.1007/978-3-031-43681-9_1
Project(s): 4SECURAIL
Metrics:
Basile D., Mazzanti F., Ferrari A.
The use of formal methods can reduce the time and costs associated with railway signalling systems development and maintenance, and improve correct behaviour and safety. The integration of formal methods into industrial model-based development tools has been the subject of recent research, indicating the potential transfer of academic techniques to enhance industrial tools. This paper explores the integration of an academic formal verification tool, UML Model Checker (UMC), with an industrial model-based development tool, Sparx Enterprise Architect (Sparx EA). The case study being analyzed is a railway standard interface. The paper demonstrates how formal verification techniques from academic tools can be integrated into industrial development practices using industrial tools, and how simulation in Sparx EA can be derived from traces generated by the UMC formal verification activity. From this experience, we derive a set of lessons learned and research challenges.Source: FMICS 2023 - 28th International Conference on Formal Methods for Industrial Critical Systems, pp. 1–21, Antwerp, Belgium, 20-22/09/2023
DOI: 10.1007/978-3-031-43681-9_1
Project(s): 4SECURAIL
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2023
Software
Unknown
Experimenting with formal verification and model-based development in railways: the case of UMC and Sparx Enterprise Architect - Complementary data
Basile D., Mazzanti F., Ferrari A.
This repository contains the UMC and SPARX EA data used in the paper: Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect by Davide Basile, Franco Mazzanti and Alessio Ferrari.DOI: 10.5281/zenodo.7920448
Project(s): 4SECURAIL
Metrics:
Basile D., Mazzanti F., Ferrari A.
This repository contains the UMC and SPARX EA data used in the paper: Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect by Davide Basile, Franco Mazzanti and Alessio Ferrari.DOI: 10.5281/zenodo.7920448
Project(s): 4SECURAIL
Metrics:
See at: CNR ExploRA
2023
Contribution to book
Open Access
REFSQ 2023: joint proceedings of workshops, doctoral symposium, posters & tools track, and journal early feedback track - Preface
Spagnolo G. O., Ferrari A., Penzenstadler B.
This document is the preface of the Joint Proceedings of Workshops, Doctoral Symposium, Posters & Tools Track, and Journal Early Feedback Track of the 29th International Working Conference on Requirement Engineering: Foundation for Software Quality (REFSQ 2023), 17th--20th April 2023, held in Barcelona, Catalunya, Spain.Source: Joint Proceedings of REFSQ-2023 Workshops, Doctoral Symposium, Posters & Tools Track and Journal Early Feedback co-located with the 28th International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2023), edited by Ferrari A. et al., 2023
Spagnolo G. O., Ferrari A., Penzenstadler B.
This document is the preface of the Joint Proceedings of Workshops, Doctoral Symposium, Posters & Tools Track, and Journal Early Feedback Track of the 29th International Working Conference on Requirement Engineering: Foundation for Software Quality (REFSQ 2023), 17th--20th April 2023, held in Barcelona, Catalunya, Spain.Source: Joint Proceedings of REFSQ-2023 Workshops, Doctoral Symposium, Posters & Tools Track and Journal Early Feedback co-located with the 28th International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2023), edited by Ferrari A. et al., 2023
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Eliciting the double-edged impact of digitalisation: a case study in rural areas
Ferrari A., Lepore F., Ortolani L., Brunori G.
Designing systems that account for sustainability concerns demands for a better understanding of the impact that digital technology interventions can have on a certain socio-technical context. However, limited studies are available about the elicitation of impact-related information from stakeholders, and strategies are particularly needed to elicit possible longterm effects, including negative ones, that go beyond the planned system goals. This paper reports a case study about the impact of digitalisation in remote mountain areas, in the context of a system for ordinary land management and hydro-geological risk control. The elicitation process was based on interviews and workshops. In the initial phase, past and present impacts were identified. In a second phase, future impacts were forecasted through the discussion of two alternative scenarios: a dystopic, technology-intensive one, and a technology-balanced one. The approach was particularly effective in identifying negative impacts. Among them, we highlight the higher stress due to the excess of connectivity, the partial reduction of decision-making abilities, and the risk of marginalisation for certain types of stakeholders. The study posits that before the elicitation of system goals, requirements engineers need to identify the socio-economic impacts of ICT technologies included in the system, as negative effects need to be properly mitigated. Our study contributes to the literature with: a set of impacts specific to the case, which can apply to similar contexts; an effective approach for impact elicitation; and a list of lessons learned from the experience.Source: RE 2023 - 31st IEEE International Requirements Engineering Conference, pp. 157–168, Hannover, Germany, 4-8/09/2023
DOI: 10.1109/re57278.2023.00024
DOI: 10.48550/arxiv.2306.05078
Metrics:
Ferrari A., Lepore F., Ortolani L., Brunori G.
Designing systems that account for sustainability concerns demands for a better understanding of the impact that digital technology interventions can have on a certain socio-technical context. However, limited studies are available about the elicitation of impact-related information from stakeholders, and strategies are particularly needed to elicit possible longterm effects, including negative ones, that go beyond the planned system goals. This paper reports a case study about the impact of digitalisation in remote mountain areas, in the context of a system for ordinary land management and hydro-geological risk control. The elicitation process was based on interviews and workshops. In the initial phase, past and present impacts were identified. In a second phase, future impacts were forecasted through the discussion of two alternative scenarios: a dystopic, technology-intensive one, and a technology-balanced one. The approach was particularly effective in identifying negative impacts. Among them, we highlight the higher stress due to the excess of connectivity, the partial reduction of decision-making abilities, and the risk of marginalisation for certain types of stakeholders. The study posits that before the elicitation of system goals, requirements engineers need to identify the socio-economic impacts of ICT technologies included in the system, as negative effects need to be properly mitigated. Our study contributes to the literature with: a set of impacts specific to the case, which can apply to similar contexts; an effective approach for impact elicitation; and a list of lessons learned from the experience.Source: RE 2023 - 31st IEEE International Requirements Engineering Conference, pp. 157–168, Hannover, Germany, 4-8/09/2023
DOI: 10.1109/re57278.2023.00024
DOI: 10.48550/arxiv.2306.05078
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | doi.org | doi.org | ieeexplore.ieee.org | CNR ExploRA
2023
Conference article
Open Access
Requirements classification for smart allocation: a case study in the railway industry
Bashir S., Abbas M., Ferrari A., Saadatmand M., Lindberg P.
Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.Source: RE 2023 - 31st IEEE International Requirements Engineering Conference, pp. 201–211, Hannover, Germany, 4-8/09/2023
DOI: 10.1109/re57278.2023.00028
Metrics:
Bashir S., Abbas M., Ferrari A., Saadatmand M., Lindberg P.
Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.Source: RE 2023 - 31st IEEE International Requirements Engineering Conference, pp. 201–211, Hannover, Germany, 4-8/09/2023
DOI: 10.1109/re57278.2023.00028
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2023
Contribution to conference
Open Access
Preface to Requirements Engineering: Foundation for Software Quality 2023
Ferrari A., Penzenstadler B.
This volume contains the papers presented at REFSQ 2023, the 29th International Working Conference on Requirements Engineering: Foundation for Software Quality, held on April 17-20, 2023 in Barcelona, Spain.
Ferrari A., Penzenstadler B.
This volume contains the papers presented at REFSQ 2023, the 29th International Working Conference on Requirements Engineering: Foundation for Software Quality, held on April 17-20, 2023 in Barcelona, Spain.
See at:
link.springer.com | CNR ExploRA
2023
Contribution to conference
Open Access
Message from the Chairs: FormaliSE 2023
Gnesi S., Plat N., Jakobs M. C., Murray T., Ferrari A., Broccia G.
This volume contains the papers presented at FormaliSE 2023: the 11th International Conference on Formal Methods in Software engineering, co-located with ICSE 2023, the 45th International Conference on Software Engineering.DOI: 10.1109/formalise58978.2023.00005
Metrics:
Gnesi S., Plat N., Jakobs M. C., Murray T., Ferrari A., Broccia G.
This volume contains the papers presented at FormaliSE 2023: the 11th International Conference on Formal Methods in Software engineering, co-located with ICSE 2023, the 45th International Conference on Software Engineering.DOI: 10.1109/formalise58978.2023.00005
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2023
Contribution to conference
Open Access
Artificial Intelligence in Engineering and society: blue skies, black holes, and the job of Requirements Engineers (Keynote)
Ferrari A.
The democratization of artificial intelligence (AI) has brought substantial achievements in science, engineering disciplines, and society as a whole. New technologies based on large language models, multi-modal learning, embodied AI, and the quest for artificial general intelligence (AGI) promise to further change the world's landscape as we know it. At the same time, AI's rapid and uncontrolled evolution also poses serious risks to society, such as the concentration of power, exclusion, discrimination, and manipulation of reality. The keynote will present some experiences in AI democratization, including the us- age of explainable machine learning approaches for agronomists, NLP-based solutions for railway engineers, image processing techniques for the maintenance of riverbeds, and mobile data processing in road safety assessment. . The talk will outline the latest technological advancements in AI, e.g., in healthcare and science, and will show how large language models like ChatGPT and Bing Chat can solve long-standing requirements engineering (RE) problems. For example, requirements completeness can be easily checked and addressed with simple prompts, and model generation from requirements becomes a one-click task. The keynote will then describe the risks that current AI development poses to society. Besides the increasingly convincing deep fakes, and the widely discussed risks for privacy and reputation, we must be aware of the uncontrolled speed of AI evolution. As AI continues to advance, it will replace many jobs that require intellectual skills. This could lead to a significant number of people losing their jobs, as they may not have the necessary skills to adapt to the new labour market. People and entire countries that cannot exploit technological developments will be excluded from the game, and this will cause resentment and the possible emergence of new fundamentalism. The race for semiconductors is already creating hot spots and rifts between the superpowers.Source: 31st IEEE International Requirements Engineering Conference, RE 2023, pp. 67–67, Hannover, Germany, 4-8/09/2023
DOI: 10.1109/rew57809.2023.00018
Metrics:
Ferrari A.
The democratization of artificial intelligence (AI) has brought substantial achievements in science, engineering disciplines, and society as a whole. New technologies based on large language models, multi-modal learning, embodied AI, and the quest for artificial general intelligence (AGI) promise to further change the world's landscape as we know it. At the same time, AI's rapid and uncontrolled evolution also poses serious risks to society, such as the concentration of power, exclusion, discrimination, and manipulation of reality. The keynote will present some experiences in AI democratization, including the us- age of explainable machine learning approaches for agronomists, NLP-based solutions for railway engineers, image processing techniques for the maintenance of riverbeds, and mobile data processing in road safety assessment. . The talk will outline the latest technological advancements in AI, e.g., in healthcare and science, and will show how large language models like ChatGPT and Bing Chat can solve long-standing requirements engineering (RE) problems. For example, requirements completeness can be easily checked and addressed with simple prompts, and model generation from requirements becomes a one-click task. The keynote will then describe the risks that current AI development poses to society. Besides the increasingly convincing deep fakes, and the widely discussed risks for privacy and reputation, we must be aware of the uncontrolled speed of AI evolution. As AI continues to advance, it will replace many jobs that require intellectual skills. This could lead to a significant number of people losing their jobs, as they may not have the necessary skills to adapt to the new labour market. People and entire countries that cannot exploit technological developments will be excluded from the game, and this will cause resentment and the possible emergence of new fundamentalism. The race for semiconductors is already creating hot spots and rifts between the superpowers.Source: 31st IEEE International Requirements Engineering Conference, RE 2023, pp. 67–67, Hannover, Germany, 4-8/09/2023
DOI: 10.1109/rew57809.2023.00018
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2022
Journal article
Open Access
Drivers, barriers and impacts of digitalisation in rural areas from the viewpoint of experts
Ferrari A., Bacco M., Gaber K., Jedlitschka A., Hess S., Kaipainen J., Koltsida P., Toli E., Brunori G.
[Context] The domain of rural areas, including rural communities, agriculture, and forestry, is going through a process of deep digital transformation. Digitalisation can have positive impacts on sustainability in terms of greater environmental control, and community prosperity. At the same time, it can also have disruptive effects, with the marginalisation of actors that cannot cope with the change. When developing a novel system for rural areas, requirements engineers should carefully consider the specific socio-economic characteristics of the domain, so that potential positive effects can be maximised, while mitigating negative impacts. [Objective] The goal of this paper is to support requirements engineers with a reference catalogue of drivers, barriers and potential impacts associated to the introduction of novel ICT solutions in rural areas. [Method] To this end, we interview 30 cross-disciplinary experts in digitalisation of rural areas, and we analyse the transcripts to identify common themes. [Results] According to the experts, main drivers are economic, with the possibility of reducing costs, and regulatory, as institutions push for more precise tracing and monitoring of production; barriers are the limited connectivity, but also distrust towards technology and other socio-cultural aspects; positive impacts are socio-economic (e.g., reduction of manual labor, greater productivity), while negative ones include potential dependency from technology, with loss of hands-on expertise, and marginalisation of certain actors (e.g., small farms, subjects with limited education). [Conclusion] This paper contributes to the literature with a domain-specific catalogue that characterises digitalisation in rural areas. The catalogue can be used as a reference baseline for requirements elicitation endeavours in rural areas, to support domain analysis prior to the development of novel solutions, as well as fit-gap analysis for the adaptation of existing technologies.Source: Information and software technology 145 (2022). doi:10.1016/j.infsof.2021.106816
DOI: 10.1016/j.infsof.2021.106816
Project(s): DESIRA
Metrics:
Ferrari A., Bacco M., Gaber K., Jedlitschka A., Hess S., Kaipainen J., Koltsida P., Toli E., Brunori G.
[Context] The domain of rural areas, including rural communities, agriculture, and forestry, is going through a process of deep digital transformation. Digitalisation can have positive impacts on sustainability in terms of greater environmental control, and community prosperity. At the same time, it can also have disruptive effects, with the marginalisation of actors that cannot cope with the change. When developing a novel system for rural areas, requirements engineers should carefully consider the specific socio-economic characteristics of the domain, so that potential positive effects can be maximised, while mitigating negative impacts. [Objective] The goal of this paper is to support requirements engineers with a reference catalogue of drivers, barriers and potential impacts associated to the introduction of novel ICT solutions in rural areas. [Method] To this end, we interview 30 cross-disciplinary experts in digitalisation of rural areas, and we analyse the transcripts to identify common themes. [Results] According to the experts, main drivers are economic, with the possibility of reducing costs, and regulatory, as institutions push for more precise tracing and monitoring of production; barriers are the limited connectivity, but also distrust towards technology and other socio-cultural aspects; positive impacts are socio-economic (e.g., reduction of manual labor, greater productivity), while negative ones include potential dependency from technology, with loss of hands-on expertise, and marginalisation of certain actors (e.g., small farms, subjects with limited education). [Conclusion] This paper contributes to the literature with a domain-specific catalogue that characterises digitalisation in rural areas. The catalogue can be used as a reference baseline for requirements elicitation endeavours in rural areas, to support domain analysis prior to the development of novel solutions, as well as fit-gap analysis for the adaptation of existing technologies.Source: Information and software technology 145 (2022). doi:10.1016/j.infsof.2021.106816
DOI: 10.1016/j.infsof.2021.106816
Project(s): DESIRA
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2022
Conference article
Open Access
A zero-shot learning approach to classifying requirements: preliminary study
Alhoshan W., Zhao L., Ferrari A., Letsholo K. J.
Context and motivation: Natural Language Processing (NLP) techniques are constantly improving their capabilities, and deep learning approaches are now used in the daily practice of several application domains. Requirements engineering (RE) research has traditionally incorporated NLP solutions to ad-dress its fundamental tasks, such as classification, tracing, and defect detection. Question/problem: However, RE research often suffers from a lack of annotated datasets, and this makes it difficult to fully exploit supervised NLP techniques in general, and deep-learning ones in the specific, thereby losing the potential advantages offered by these techniques. Principal ideas/results: To address the problem of limited annotated datasets, we propose to use zero-shot classification, and apply this learning paradigm to RE tasks that can be treated as classification problems. We experimented with the task of distinguishing between two types of NFR requirements: usability and security requirement and obtained encouraging weighted F-scores over 80% and almost perfect recall rates from a number of the tested models, without any training data and fine-tuning. Contribution: This work paves the basis for further research in the application of zero-shot learning, and towards the solution of the long-standing problem of dataset annotation in RE.Source: REFSQ 2022 - 28th International Working Conference on Requirement Engineering: Foundation for Software Quality, pp. 52–59, Birmingham, UK, 21-24/03/2022
DOI: 10.1007/978-3-030-98464-9_5
Metrics:
Alhoshan W., Zhao L., Ferrari A., Letsholo K. J.
Context and motivation: Natural Language Processing (NLP) techniques are constantly improving their capabilities, and deep learning approaches are now used in the daily practice of several application domains. Requirements engineering (RE) research has traditionally incorporated NLP solutions to ad-dress its fundamental tasks, such as classification, tracing, and defect detection. Question/problem: However, RE research often suffers from a lack of annotated datasets, and this makes it difficult to fully exploit supervised NLP techniques in general, and deep-learning ones in the specific, thereby losing the potential advantages offered by these techniques. Principal ideas/results: To address the problem of limited annotated datasets, we propose to use zero-shot classification, and apply this learning paradigm to RE tasks that can be treated as classification problems. We experimented with the task of distinguishing between two types of NFR requirements: usability and security requirement and obtained encouraging weighted F-scores over 80% and almost perfect recall rates from a number of the tested models, without any training data and fine-tuning. Contribution: This work paves the basis for further research in the application of zero-shot learning, and towards the solution of the long-standing problem of dataset annotation in RE.Source: REFSQ 2022 - 28th International Working Conference on Requirement Engineering: Foundation for Software Quality, pp. 52–59, Birmingham, UK, 21-24/03/2022
DOI: 10.1007/978-3-030-98464-9_5
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2022
Conference article
Open Access
Towards explainable formal methods: from LTL to natural language with neural machine translation
Cherukuri H., Ferrari A., Spoletini P.
[Context and motivation] Requirements formalisation facilitates reasoning about inconsistencies, detection of ambiguities and identification of critical issues in system models. Temporal logic formulae are the natural choice when it comes to formalise requirements associated to desired system behaviours. [Ques tion/problem] Understanding and mastering temporal logic require a formal background. Means are therefore needed to make temporal logic formulae interpretable by engineers, domain experts and other stakeholders involved in the development process. [Principal ideas/results] In this paper, we propose to use a neural machine translation tool, named OPENNMT, to translate Linear Temporal Logic (LTL) formulae into corresponding natural language descriptions. Our results show that our translation system achieves an average BLEU (BiLingual Evaluation Understudy) score of 93.53%, which corresponds to high-quality translations. [Contribution] Our neural model can be applied to assess if requirements have been correctly formalised. This can be useful to requirements analysts, who may have limited confidence with LTL, and to other stakeholders involved in the requirements verification process. Overall, our research preview contributes to bridging the gap between formal methods and requirements engineering, and opens to further research in explainable formal methods.Source: REFSQ 2022 - 28th International Working Conference on Requirement Engineering: Foundation for Software Quality, pp. 79–86, Birmingham, UK, 21-24/03/2022
DOI: 10.1007/978-3-030-98464-9_7
Project(s): 4SECURAIL
Metrics:
Cherukuri H., Ferrari A., Spoletini P.
[Context and motivation] Requirements formalisation facilitates reasoning about inconsistencies, detection of ambiguities and identification of critical issues in system models. Temporal logic formulae are the natural choice when it comes to formalise requirements associated to desired system behaviours. [Ques tion/problem] Understanding and mastering temporal logic require a formal background. Means are therefore needed to make temporal logic formulae interpretable by engineers, domain experts and other stakeholders involved in the development process. [Principal ideas/results] In this paper, we propose to use a neural machine translation tool, named OPENNMT, to translate Linear Temporal Logic (LTL) formulae into corresponding natural language descriptions. Our results show that our translation system achieves an average BLEU (BiLingual Evaluation Understudy) score of 93.53%, which corresponds to high-quality translations. [Contribution] Our neural model can be applied to assess if requirements have been correctly formalised. This can be useful to requirements analysts, who may have limited confidence with LTL, and to other stakeholders involved in the requirements verification process. Overall, our research preview contributes to bridging the gap between formal methods and requirements engineering, and opens to further research in explainable formal methods.Source: REFSQ 2022 - 28th International Working Conference on Requirement Engineering: Foundation for Software Quality, pp. 79–86, Birmingham, UK, 21-24/03/2022
DOI: 10.1007/978-3-030-98464-9_7
Project(s): 4SECURAIL
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2022
Journal article
Open Access
On the relationship between similar requirements and similar software: a case study in the railway domain
Abbas M., Ferrari A., Shatnawi A., Enoiu E., Saadatm M., Sundmark D.
Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspects come into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.Source: Requirements engineering (Lond., Internet) (2022). doi:10.1007/s00766-021-00370-4
DOI: 10.1007/s00766-021-00370-4
Metrics:
Abbas M., Ferrari A., Shatnawi A., Enoiu E., Saadatm M., Sundmark D.
Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspects come into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.Source: Requirements engineering (Lond., Internet) (2022). doi:10.1007/s00766-021-00370-4
DOI: 10.1007/s00766-021-00370-4
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2022
Journal article
Restricted
Formal Methods in railways: a systematic mapping study
Ferrari A., Ter Beek M. H.
Formal methods are mathematically based techniques for the rigorous development of software-intensive systems. The railway signaling domain is a field in which formal methods have traditionally been applied, with several success stories. This article reports on a mapping study that surveys the landscape of research on applications of formal methods to the development of railway systems. Following the guidelines of systematic reviews, we identify 328 relevant primary studies, and extract information about their demographics, the characteristics of formal methods used and railway-specific aspects. Our main results are as follows: (i) we identify a total of 328 primary studies relevant to our scope published between 1989 and 2020, of which 44% published during the last 5 years and 24% involving industry; (ii) the majority of studies are evaluated through Examples (41%) and Experience Reports (38%), while full-fledged Case Studies are limited (1.5%); (iii) Model checking is the most commonly adopted technique (47%), followed by simulation (27%) and theorem proving (19.5%); (iv) the dominant languages are UML (18%) and B (15%), while frequently used tools are ProB (9%), NuSMV (8%) and UPPAAL (7%); however, a diverse landscape of languages and tools is employed; (v) the majority of systems are interlocking products (40%), followed by models of high-level control logic (27%); (vi) most of the studies focus on the Architecture (66%) and Detailed Design (45%) development phases. Based on these findings, we highlight current research gaps and expected actions. In particular, the need to focus on more empirically sound research methods, such as Case Studies and Controlled Experiments, and to lower the degree of abstraction, by applying formal methods and tools to development phases that are closer to software development. Our study contributes with an empirically based perspective on the future of research and practice in formal methods applications for railways. It can be used by formal methods researchers to better focus their scientific inquiries, and by railway practitioners for an improved understanding of the interplay between formal methods and their specific application domain.Source: ACM computing surveys 55 (2022). doi:10.1145/3520480
DOI: 10.1145/3520480
Project(s): 4SECURAIL, ASTRail
Metrics:
Ferrari A., Ter Beek M. H.
Formal methods are mathematically based techniques for the rigorous development of software-intensive systems. The railway signaling domain is a field in which formal methods have traditionally been applied, with several success stories. This article reports on a mapping study that surveys the landscape of research on applications of formal methods to the development of railway systems. Following the guidelines of systematic reviews, we identify 328 relevant primary studies, and extract information about their demographics, the characteristics of formal methods used and railway-specific aspects. Our main results are as follows: (i) we identify a total of 328 primary studies relevant to our scope published between 1989 and 2020, of which 44% published during the last 5 years and 24% involving industry; (ii) the majority of studies are evaluated through Examples (41%) and Experience Reports (38%), while full-fledged Case Studies are limited (1.5%); (iii) Model checking is the most commonly adopted technique (47%), followed by simulation (27%) and theorem proving (19.5%); (iv) the dominant languages are UML (18%) and B (15%), while frequently used tools are ProB (9%), NuSMV (8%) and UPPAAL (7%); however, a diverse landscape of languages and tools is employed; (v) the majority of systems are interlocking products (40%), followed by models of high-level control logic (27%); (vi) most of the studies focus on the Architecture (66%) and Detailed Design (45%) development phases. Based on these findings, we highlight current research gaps and expected actions. In particular, the need to focus on more empirically sound research methods, such as Case Studies and Controlled Experiments, and to lower the degree of abstraction, by applying formal methods and tools to development phases that are closer to software development. Our study contributes with an empirically based perspective on the future of research and practice in formal methods applications for railways. It can be used by formal methods researchers to better focus their scientific inquiries, and by railway practitioners for an improved understanding of the interplay between formal methods and their specific application domain.Source: ACM computing surveys 55 (2022). doi:10.1145/3520480
DOI: 10.1145/3520480
Project(s): 4SECURAIL
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