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, Melbourne, Australia, 14-20/05/2023
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, Melbourne, Australia, 14-20/05/2023
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| CNR ExploRA
2023
Journal article
Open Access
Classification and mapping of riparian vegetation with high-resolution aerial orthoimages and machine learning algorithms
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: IEEE geoscience and remote sensing letters (Online) (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: IEEE geoscience and remote sensing letters (Online) (2023).
Project(s): DESIRA
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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 (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 (2023). doi:10.1016/j.infsof.2023.107202
DOI: 10.1016/j.infsof.2023.107202
Metrics:
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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
Metrics:
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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
Metrics:
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ISTI Repository | link.springer.com 
| CNR ExploRA
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
Metrics:
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ISTI Repository | link.springer.com | CNR ExploRA
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
Metrics:
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ISTI Repository | link.springer.com | CNR ExploRA
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
, ASTRail Metrics:
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dl.acm.org | CNR ExploRA
2022
Journal article
Open Access
Exploring the ERTMS/ETCS full moving block specification: an experience with formal methods
Basile D., Ter Beek M. H., Ferrari A., Legay A.
Shift2Rail is a joint undertaking funded by the EU via its Horizon 2020 program and by main railway stakeholders. Several Shift2Rail projects aim to investigate the application of formal methods to new ERTMS/ETCS railway signalling systems that promise to move European railway forward by guaranteeing high capacity, low cost and improved reliability. We explore the ERTMS/ETCS level 3 full moving block specifications stemming from different Shift2Rail projects using UPPAAL and statistical model checking. The results range from novel rigorously formalised requirements to an operational model formally verified against scenarios with multiple trains on a single railway line. From the gained experience, we have distilled future research goals to improve the formal specification and verification of real-time systems, and we discuss some barriers concerning a possible uptake of formal methods and tools in the railway industrySource: International journal on software tools for technology transfer (Internet) 24 (2022): 351–370. doi:10.1007/s10009-022-00653-3
DOI: 10.1007/s10009-022-00653-3
Project(s): 4SECURAIL, ASTRail
Metrics:
Basile D., Ter Beek M. H., Ferrari A., Legay A.
Shift2Rail is a joint undertaking funded by the EU via its Horizon 2020 program and by main railway stakeholders. Several Shift2Rail projects aim to investigate the application of formal methods to new ERTMS/ETCS railway signalling systems that promise to move European railway forward by guaranteeing high capacity, low cost and improved reliability. We explore the ERTMS/ETCS level 3 full moving block specifications stemming from different Shift2Rail projects using UPPAAL and statistical model checking. The results range from novel rigorously formalised requirements to an operational model formally verified against scenarios with multiple trains on a single railway line. From the gained experience, we have distilled future research goals to improve the formal specification and verification of real-time systems, and we discuss some barriers concerning a possible uptake of formal methods and tools in the railway industrySource: International journal on software tools for technology transfer (Internet) 24 (2022): 351–370. doi:10.1007/s10009-022-00653-3
DOI: 10.1007/s10009-022-00653-3
Project(s): 4SECURAIL
, ASTRail Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis
Ferrari A., Spoletini P., Debnath S.
Requirements are elicited from the customer and other stakeholders through an iterative process of interviews, prototyping, and other interactive sessions. Then, requirements can be further extended, based on the analysis of the features of competing products available on the market. Understanding how this process takes place can help to identify the contribution of the different elicitation phases, thereby allowing requirements analysts to better distribute their resources. In this work, we empirically study in which way requirements get transformed from initial ideas into documented needs, and then evolve based on the inspiration coming from similar products. To this end, we select 30 subjects that act as requirements analysts, and we perform interview-based elicitation sessions with a fictional customer. After the sessions, the analysts produce a first set of requirements for the system. Then, they are required to search similar products in the app stores and extend the requirements, inspired by the identified apps. The requirements documented at each step are evaluated, to assess to which extent and in which way the initial idea evolved throughout the process. Our results show that only between 30% and 38% of the requirements produced after the interviews include content that can be fully traced to initial customer's ideas. The rest of the content is dedicated to new requirements, and up to 21% of it belongs to completely novel topics. Furthermore, up to 42% of the requirements inspired by the app stores cover additional features compared to the ones identified after the interviews. The results empirically show that requirements are not elicited in strict sense, but actually co-created through interviews, with analysts playing a crucial role in the process. In addition, we show evidence that app store-inspired elicitation can be particularly beneficial to complete the requirements.Source: Requirements engineering (Lond., Print) (2022). doi:10.1007/s00766-022-00383-7
DOI: 10.1007/s00766-022-00383-7
Metrics:
Ferrari A., Spoletini P., Debnath S.
Requirements are elicited from the customer and other stakeholders through an iterative process of interviews, prototyping, and other interactive sessions. Then, requirements can be further extended, based on the analysis of the features of competing products available on the market. Understanding how this process takes place can help to identify the contribution of the different elicitation phases, thereby allowing requirements analysts to better distribute their resources. In this work, we empirically study in which way requirements get transformed from initial ideas into documented needs, and then evolve based on the inspiration coming from similar products. To this end, we select 30 subjects that act as requirements analysts, and we perform interview-based elicitation sessions with a fictional customer. After the sessions, the analysts produce a first set of requirements for the system. Then, they are required to search similar products in the app stores and extend the requirements, inspired by the identified apps. The requirements documented at each step are evaluated, to assess to which extent and in which way the initial idea evolved throughout the process. Our results show that only between 30% and 38% of the requirements produced after the interviews include content that can be fully traced to initial customer's ideas. The rest of the content is dedicated to new requirements, and up to 21% of it belongs to completely novel topics. Furthermore, up to 42% of the requirements inspired by the app stores cover additional features compared to the ones identified after the interviews. The results empirically show that requirements are not elicited in strict sense, but actually co-created through interviews, with analysts playing a crucial role in the process. In addition, we show evidence that app store-inspired elicitation can be particularly beneficial to complete the requirements.Source: Requirements engineering (Lond., Print) (2022). doi:10.1007/s00766-022-00383-7
DOI: 10.1007/s00766-022-00383-7
Metrics:
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link.springer.com | ISTI Repository | ISTI Repository | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Empirical software engineering and formal methods for IoT systems
Basile D., Ter Beek M. H., Broccia G., Ferrari A.
Researchers from the Formal Methods and Tools (FMT) lab of ISTI-CNR are working on the application of formal methods to devise interaction protocols for safe-by-construction IoT Systems of Systems. They are also working on the empirical investigation and evaluation of the effectiveness of techniques and methodologies proposed for IoT application scenarios. The research is being conducted in the context of the national project T-LADIES, funded by the Italian Ministry of Education, University and Research (MIUR) under the program for Projects of National Interest (PRIN).Source: ERCIM news 131 (2022): 34–35.
Basile D., Ter Beek M. H., Broccia G., Ferrari A.
Researchers from the Formal Methods and Tools (FMT) lab of ISTI-CNR are working on the application of formal methods to devise interaction protocols for safe-by-construction IoT Systems of Systems. They are also working on the empirical investigation and evaluation of the effectiveness of techniques and methodologies proposed for IoT application scenarios. The research is being conducted in the context of the national project T-LADIES, funded by the Italian Ministry of Education, University and Research (MIUR) under the program for Projects of National Interest (PRIN).Source: ERCIM news 131 (2022): 34–35.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Empirical formal methods: guidelines for performing empirical studies on formal methods
Ter Beek M. H., Ferrari A.
Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients and then define nine different study strategies (usability testing, laboratory experiments with software and human subjects, case studies, qualitative studies, surveys, judgement studies, systematic literature reviews, and systematic mapping studies) and discuss for each of them their crucial characteristics, the difficulties of applying them to formal methods and tools, typical threats to validity, their maturity in formal methods, pointers to external guidelines, and pointers to studies in other fields. We conclude with a number of challenges for empirical formal methods.Source: Software (Basel) 1 (2022): 381–416. doi:10.3390/software1040017
DOI: 10.3390/software1040017
Metrics:
Ter Beek M. H., Ferrari A.
Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients and then define nine different study strategies (usability testing, laboratory experiments with software and human subjects, case studies, qualitative studies, surveys, judgement studies, systematic literature reviews, and systematic mapping studies) and discuss for each of them their crucial characteristics, the difficulties of applying them to formal methods and tools, typical threats to validity, their maturity in formal methods, pointers to external guidelines, and pointers to studies in other fields. We conclude with a number of challenges for empirical formal methods.Source: Software (Basel) 1 (2022): 381–416. doi:10.3390/software1040017
DOI: 10.3390/software1040017
Metrics:
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ISTI Repository | CNR ExploRA | www.mdpi.com
2022
Conference article
Open Access
Safe and secure future AI-driven railway technologies: challenges for formal methods in railway
Seisenberger M., Ter Beek M. H., Fan X., Ferrari A., Haxthausen A., James P., Lawrence A., Luttik B., Van De Pol J., Wimmer S.
In 2020, the EU launched its sustainable and smart mobility strategy, outlining how it plans to have a 90% reduction in transport emission by 2050. Central to achieving this goal will be the improvement of rail technology, with many new data-driven visionary systems being proposed. AI will be the enabling technology for many of those systems. However, safety and security guarantees will be key for wide-spread acceptance and uptake by Industry and Society. Therefore, suitable verification and validation techniques are needed. In this article, we argue how formal methods research can contribute to the development of modern Railway systems -- which may or may not make use of AI techniques -- and present several research problems and techniques worth to be further considered.Source: ISoLA'22 - 11th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, pp. 246–268, Rhodes, Greece, 24-28/10/2022
DOI: 10.1007/978-3-031-19762-8_20
Metrics:
Seisenberger M., Ter Beek M. H., Fan X., Ferrari A., Haxthausen A., James P., Lawrence A., Luttik B., Van De Pol J., Wimmer S.
In 2020, the EU launched its sustainable and smart mobility strategy, outlining how it plans to have a 90% reduction in transport emission by 2050. Central to achieving this goal will be the improvement of rail technology, with many new data-driven visionary systems being proposed. AI will be the enabling technology for many of those systems. However, safety and security guarantees will be key for wide-spread acceptance and uptake by Industry and Society. Therefore, suitable verification and validation techniques are needed. In this article, we argue how formal methods research can contribute to the development of modern Railway systems -- which may or may not make use of AI techniques -- and present several research problems and techniques worth to be further considered.Source: ISoLA'22 - 11th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, pp. 246–268, Rhodes, Greece, 24-28/10/2022
DOI: 10.1007/978-3-031-19762-8_20
Metrics:
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ISTI Repository | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
Rethinking requirements engineering for sustainability
Ferrari A., Bacco M.
We present a new paradigm of requirements engineering research for sustainability. This study proposes to go beyond stakeholders' goals, and introduces the concepts of drivers, barriers and impacts of technology in a certain domain. We collect information about these constructs in an interview study with 30 experts on digitalisation in forestry, agriculture, and rural areas.Source: ERCIM news online edition 131 (2022): 27–28.
Project(s): DESIRA
Ferrari A., Bacco M.
We present a new paradigm of requirements engineering research for sustainability. This study proposes to go beyond stakeholders' goals, and introduces the concepts of drivers, barriers and impacts of technology in a certain domain. We collect information about these constructs in an interview study with 30 experts on digitalisation in forestry, agriculture, and rural areas.Source: ERCIM news online edition 131 (2022): 27–28.
Project(s): DESIRA
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2022
Contribution to conference
Open Access
Co-design of technological solutions for agriculture and rural areas: methodology and cases for responsible innovation
Bacco F. M., Ferrari A., Brunori G.
The main results of the H2020 DESIRA project will be presented, focusing on the so-called use cases, i.e., the co-design of 5 high-level technological solutions in 21 EU Living Labs to push for sustainable digital tools tailored to the context to be introduced in forestry, agricultural, and rural areas. Both the methodology and the main results will be shown, focusing on the 5 case studies developed in the project.Source: IEEE 8th World Forum on Internet of Things - Vertical Track: Agriculture, Yokohama, Japan, 26/10/2022-11/11/2022
Project(s): DESIRA
Bacco F. M., Ferrari A., Brunori G.
The main results of the H2020 DESIRA project will be presented, focusing on the so-called use cases, i.e., the co-design of 5 high-level technological solutions in 21 EU Living Labs to push for sustainable digital tools tailored to the context to be introduced in forestry, agricultural, and rural areas. Both the methodology and the main results will be shown, focusing on the 5 case studies developed in the project.Source: IEEE 8th World Forum on Internet of Things - Vertical Track: Agriculture, Yokohama, Japan, 26/10/2022-11/11/2022
Project(s): DESIRA
See at:
ISTI Repository | CNR ExploRA
2022
Contribution to conference
Open Access
AIRE 2022: 9th international workshop on Artificial Intelligence and Requirements Engineering
Ferrari A., Heyn H. M., Sabetzadeh M.
RE researchers have employed AI techniques to tackle different notions of requirements quality, have applied the techniques to different case studies and domains, and have used different metrics to assess the performance of their techniques. Given the pervasiveness of AI-based systems in our daily life, recent years have also seen an increasing need for RE techniques to support sound and structured development of AI system, with particular interest in explainability of system behaviour. The primary purpose of the AIRE workshop is to explore synergies between AI and RE in order to identify complex RE problems that could benefit from the application of AI techniques and the other way round, thus addressing RE for AI challenges. The edition of the workshop in 2022 received 6 submissions, which were independently reviewed by at least three program committee members. In the end, 5 papers were accepted. All the conflicts of interest were treated seriously and independently. The workshop takes place virtually on August 16, 2022. We hope that you enjoy the AIRE'22 workshop and its proceedings. We consider that in the days when AI is gaining prominence in our daily lives, the RE community cannot neglect the benefit that AI techniques can deliver to the practice of requirements engineering. The workshop will feature also two keynotes, from Fabiano Dalpiaz, from the University of Utrecht, the Netherlands, on Requirements Conversations: A New Frontier in Alfor-RE, and from Jennifer Horkoff, on Requirements Engineering for Machine Learning: Non-functional Requirements as Core Functions. We look forward to seeing you all at this workshop and the future editions. We are very grateful to the Program Committee members and authors of the submissions for their hard work and dedication in putting together this program. We would like to thank you all for your participation in AIRE'22.Source: REW 2022 - 30th IEEE International Requirements Engineering Conference Workshops, pp. 139–140, Online conference, 15-20/08/2022
DOI: 10.1109/rew56159.2022.00033
Metrics:
Ferrari A., Heyn H. M., Sabetzadeh M.
RE researchers have employed AI techniques to tackle different notions of requirements quality, have applied the techniques to different case studies and domains, and have used different metrics to assess the performance of their techniques. Given the pervasiveness of AI-based systems in our daily life, recent years have also seen an increasing need for RE techniques to support sound and structured development of AI system, with particular interest in explainability of system behaviour. The primary purpose of the AIRE workshop is to explore synergies between AI and RE in order to identify complex RE problems that could benefit from the application of AI techniques and the other way round, thus addressing RE for AI challenges. The edition of the workshop in 2022 received 6 submissions, which were independently reviewed by at least three program committee members. In the end, 5 papers were accepted. All the conflicts of interest were treated seriously and independently. The workshop takes place virtually on August 16, 2022. We hope that you enjoy the AIRE'22 workshop and its proceedings. We consider that in the days when AI is gaining prominence in our daily lives, the RE community cannot neglect the benefit that AI techniques can deliver to the practice of requirements engineering. The workshop will feature also two keynotes, from Fabiano Dalpiaz, from the University of Utrecht, the Netherlands, on Requirements Conversations: A New Frontier in Alfor-RE, and from Jennifer Horkoff, on Requirements Engineering for Machine Learning: Non-functional Requirements as Core Functions. We look forward to seeing you all at this workshop and the future editions. We are very grateful to the Program Committee members and authors of the submissions for their hard work and dedication in putting together this program. We would like to thank you all for your participation in AIRE'22.Source: REW 2022 - 30th IEEE International Requirements Engineering Conference Workshops, pp. 139–140, Online conference, 15-20/08/2022
DOI: 10.1109/rew56159.2022.00033
Metrics:
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ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2021
Report
Open Access
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. 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.Source: ISTI-TR-2021/006, 2021
DOI: 10.32079/isti-tr-2021/006
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. 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.Source: ISTI-TR-2021/006, 2021
DOI: 10.32079/isti-tr-2021/006
Project(s): 4SECURAIL
, ASTRail Metrics:
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ISTI Repository | CNR ExploRA
2021
Report
Open Access
Systematic evaluation and usability analysis of formal tools for railway system design
Ferrari A., Mazzanti F., Basile D., Ter Beek M. H.
Formal methods and supporting tools have a long record of success in the development of safety-critical systems. However, no single tool has emerged as the dominant solution for system design. Each tool differs from the others in terms of the modeling language used, its verification capabilities and other complementary features, and each development context has peculiar needs that require different tools. This is particularly problematic for the railway industry, in which formal methods are highly recommended by the norms, but no actual guidance is provided for the selection of tools. To guide companies in the selection of the most appropriate formal tools to adopt in their contexts, a clear assessment of the features of the currently available tools is required. To address this goal, this paper considers a set of 13 formal tools that have been used for railway system design, and it presents a systematic evaluation of such tools and a preliminary usability analysis of a subset of 7 tools, involving railway practitioners. The results are discussed considering the most desired aspects by industry and earlier related studies. While the focus is on the railway domain, the overall methodology can be applied to similar contexts. Our study thus contributes with a systematic evaluation of formal tools and it shows that despite the poor graphical interfaces, usability and maturity of the tools are not major problems, as claimed by contributions from the literature. Instead, support for process integration is the most relevant obstacle for the adoption of most of the tools.Source: ISTI-2021-TR/007, 2021
DOI: 10.32079/isti-tr-2021/007
Project(s): 4SECURAIL, ASTRail
Metrics:
Ferrari A., Mazzanti F., Basile D., Ter Beek M. H.
Formal methods and supporting tools have a long record of success in the development of safety-critical systems. However, no single tool has emerged as the dominant solution for system design. Each tool differs from the others in terms of the modeling language used, its verification capabilities and other complementary features, and each development context has peculiar needs that require different tools. This is particularly problematic for the railway industry, in which formal methods are highly recommended by the norms, but no actual guidance is provided for the selection of tools. To guide companies in the selection of the most appropriate formal tools to adopt in their contexts, a clear assessment of the features of the currently available tools is required. To address this goal, this paper considers a set of 13 formal tools that have been used for railway system design, and it presents a systematic evaluation of such tools and a preliminary usability analysis of a subset of 7 tools, involving railway practitioners. The results are discussed considering the most desired aspects by industry and earlier related studies. While the focus is on the railway domain, the overall methodology can be applied to similar contexts. Our study thus contributes with a systematic evaluation of formal tools and it shows that despite the poor graphical interfaces, usability and maturity of the tools are not major problems, as claimed by contributions from the literature. Instead, support for process integration is the most relevant obstacle for the adoption of most of the tools.Source: ISTI-2021-TR/007, 2021
DOI: 10.32079/isti-tr-2021/007
Project(s): 4SECURAIL
, ASTRail Metrics:
See at:
CNR ExploRA
2021
Contribution to book
Open Access
Preface: 4th Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 2021)
Abualhaija S., Aydemir F. B., Ferrari A., Guo J.
The Natural Language Processing for Requirements Engineering Workshop (NLP4RE) was established in 2018 as a venue to foster communication between researchers and practitioners interested in the field. The 2021 edition was held virtually in Essen, due to the COVID-19 pandemic, and saw the presentation of 10 papers covering different aspects of NLP4RE, including information extraction (e.g., rationale, causality), requirements classification and chat-bots. The workshop saw a lively participation, with over 25 participants during the keynote and about 20 participants during the paper presentation sessions.Source: REFSQ 2021: Joint Proceedings of Workshops, OpenRE, Posters and Tools Track, and Doctoral Symposium, edited by F. B. Aydemir, C. Gralha, 2021
Abualhaija S., Aydemir F. B., Ferrari A., Guo J.
The Natural Language Processing for Requirements Engineering Workshop (NLP4RE) was established in 2018 as a venue to foster communication between researchers and practitioners interested in the field. The 2021 edition was held virtually in Essen, due to the COVID-19 pandemic, and saw the presentation of 10 papers covering different aspects of NLP4RE, including information extraction (e.g., rationale, causality), requirements classification and chat-bots. The workshop saw a lively participation, with over 25 participants during the keynote and about 20 participants during the paper presentation sessions.Source: REFSQ 2021: Joint Proceedings of Workshops, OpenRE, Posters and Tools Track, and Doctoral Symposium, edited by F. B. Aydemir, C. Gralha, 2021
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2021
Conference article
Open Access
Is requirements similarity a good proxy for software similarity? An empirical investigation in industry
Abbas M., Ferrari A., Shatnawi A., Enoiu E. P., Saadatmand M.
[Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman's rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. 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 categorization.Source: REFSQ 2021 - 27th International Working Conference on Requirements Engineering: Foundation for Software Quality, pp. 3–18, Online conference, 12-15/04/2021
DOI: 10.1007/978-3-030-73128-1_1
Metrics:
Abbas M., Ferrari A., Shatnawi A., Enoiu E. P., Saadatmand M.
[Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman's rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. 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 categorization.Source: REFSQ 2021 - 27th International Working Conference on Requirements Engineering: Foundation for Software Quality, pp. 3–18, Online conference, 12-15/04/2021
DOI: 10.1007/978-3-030-73128-1_1
Metrics:
See at:
ISTI Repository | link.springer.com | link.springer.com | CNR ExploRA