Publications
OUR RESEARCH
Scientific Publications
Here you can find the comprehensive list of publications from the members of the Research Center on Computer Vision and eXtended Reality (xRAI).
Use the tag cloud to filter papers based on specific research topics, or use the menus to filter by year, type of publication, or authors.
For each paper, you have the option to view additional details such as the Abstract, Links, and BibTex record.
Research is formalized curiosity. It is poking and prying with a purpose
Zora Neale Hurston
2024
Recupito, Gilberto; Pecorelli, Fabiano; Catolino, Gemma; Lenarduzzi, Valentina; Taibi, Davide; Nucci, Dario Di; Palomba, Fabio
Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture Journal Article
In: Journal of Systems and Software, vol. 216, pp. 112151, 2024, ISSN: 0164-1212.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Software Engineering, Technical Debt Management
@article{recupitoTechnicalDebtAIenabled2024,
title = {Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture},
author = {Gilberto Recupito and Fabiano Pecorelli and Gemma Catolino and Valentina Lenarduzzi and Davide Taibi and Dario Di Nucci and Fabio Palomba},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121224001961},
doi = {10.1016/j.jss.2024.112151},
issn = {0164-1212},
year = {2024},
date = {2024-10-01},
urldate = {2024-07-07},
journal = {Journal of Systems and Software},
volume = {216},
pages = {112151},
abstract = {Context: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems' quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems' quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective: In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers' awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method: We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results: The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion: We conclude the article by distilling lessons learned and actionable insights for researchers.},
keywords = {Artificial Intelligence, Software Engineering, Technical Debt Management},
pubstate = {published},
tppubtype = {article}
}
Context: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems' quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems' quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective: In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers' awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method: We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results: The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion: We conclude the article by distilling lessons learned and actionable insights for researchers.
Falco, Salvatore Esposito De; Montera, Raffaella; Leo, Sabrina; Laviola, Francesco; Vito, Pietro; Sardanelli, Domenico; Basile, Gianpaolo; Nevi, Giulia; Alaia, Raffaele
Trends and patterns in ESG research: A bibliometric odyssey and research agenda Journal Article
In: Corporate Social Responsibility and Environmental Management, vol. 31, no. 5, pp. 3703–3723, 2024, ISSN: 1535-3958, 1535-3966.
Abstract | Links | BibTeX | Tags: ESG, Strategy and Management, Sustainability
@article{de_falco_trends_2024,
title = {Trends and patterns in ESG research: A bibliometric odyssey and research agenda},
author = {Salvatore Esposito De Falco and Raffaella Montera and Sabrina Leo and Francesco Laviola and Pietro Vito and Domenico Sardanelli and Gianpaolo Basile and Giulia Nevi and Raffaele Alaia},
url = {https://onlinelibrary.wiley.com/doi/10.1002/csr.2744},
doi = {10.1002/csr.2744},
issn = {1535-3958, 1535-3966},
year = {2024},
date = {2024-09-01},
urldate = {2024-10-07},
journal = {Corporate Social Responsibility and Environmental Management},
volume = {31},
number = {5},
pages = {3703–3723},
abstract = {The paper provides a detailed analysis of the ESG literature with the aim of bringing clarity to this area of research and proposing a research agenda. An overview of the development of the literature on ESG pillars is offered through a review of 903 peer‐reviewed articles. The paper identifies four thematic clusters: impacts of ESG disclosure and practices, sustainability, accounting, and responsible investments. The main research streams and sub‐streams for each cluster are discussed, highlighting the most frequent theoretical perspectives and methodologies. Furthermore, the evolution of ESG research across time is delineated. The paper also identifies future research directions within each cluster to advance knowledge, and proposes an integrative framework based on focal themes and their reciprocal connections.},
keywords = {ESG, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {article}
}
The paper provides a detailed analysis of the ESG literature with the aim of bringing clarity to this area of research and proposing a research agenda. An overview of the development of the literature on ESG pillars is offered through a review of 903 peer‐reviewed articles. The paper identifies four thematic clusters: impacts of ESG disclosure and practices, sustainability, accounting, and responsible investments. The main research streams and sub‐streams for each cluster are discussed, highlighting the most frequent theoretical perspectives and methodologies. Furthermore, the evolution of ESG research across time is delineated. The paper also identifies future research directions within each cluster to advance knowledge, and proposes an integrative framework based on focal themes and their reciprocal connections.
Falco, Salvatore Esposito De; Montera, Raffaella; Cucari, Nicola
Deconstructing Corporate Purpose: A Conceptual Framework in an Evolutionary Perspective Journal Article
In: Academy of Management Proceedings, vol. 2024, no. 1, pp. 17207, 2024, ISSN: 0065-0668, 2151-6561.
Abstract | Links | BibTeX | Tags: Entrepreneurship, Strategy and Management, Sustainability
@article{esposito_de_falco_deconstructing_2024,
title = {Deconstructing Corporate Purpose: A Conceptual Framework in an Evolutionary Perspective},
author = {Salvatore Esposito De Falco and Raffaella Montera and Nicola Cucari},
url = {http://journals.aom.org/doi/full/10.5465/AMPROC.2024.17207abstract},
doi = {10.5465/AMPROC.2024.17207abstract},
issn = {0065-0668, 2151-6561},
year = {2024},
date = {2024-08-01},
urldate = {2024-10-07},
journal = {Academy of Management Proceedings},
volume = {2024},
number = {1},
pages = {17207},
abstract = {This paper introduces a novel multi-dimensional co-evolutionary framework for understanding and analysing corporate purpose, addressing its under-conceptualized nature and diverse interpretations in contemporary business studies. Grounded in the principles of coevolution and integrating Esposito De Falco’s (2012) framework on the genesis and evolution of firms, our work advances a unique theoretical typology of corporate purpose. This typology elucidates the evolutionary pathway of corporate purpose through the "3S" dimensions (structural, systemic, and strategic), offering organizations a guide to align their strategies with both their identity and public image. By examining corporate purpose through these dimensions, we highlight the interconnectedness of an organization’s intrinsic identity (structural), its engagement within its ecosystem (systemic), and the alignment of purpose with actionable strategies (strategic). This multi-dimensional approach reveals how corporate purpose guides firms through a transformative journey from their genesis to their interactions with stakeholders and broader market dynamics. To do this, the paper delineates four distinct typologies of corporate purpose: purpose washing, formal purpose, promising purpose, and deep purpose. These typologies are systematically organized within a synthesis matrix, providing a nuanced and detailed understanding of how corporate purpose manifests and evolves within different organizational contexts. Central to the study are two key propositions. The first proposition positions corporate purpose as the “metabolism of firms”, drawing a parallel with biological metabolism to underscore its crucial role in ensuring business survival, prosperity, and evolutionary adaptation. The second proposition emphasizes the importance of a sequential progression across the genetic, relational, and phenotypic stages of corporate purpose, which is critical for the design of genuinely purposeful organizations. By moving beyond a purely definitional approach, this paper contributes significantly to the ontological understanding of corporate purpose. It sheds light on the dynamics of purpose in organizations, highlighting its strategic importance and the need for alignment between a firm’s identity and actions. Our paper provides a valuable resource for academics and practitioners alike, seeking to navigate the complexities of corporate purpose in a dynamic business landscape. Keywords: Corporate purpose; evolutionary perspective; genotype; relational dimension; phenotype; corporate purpose typologies},
keywords = {Entrepreneurship, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a novel multi-dimensional co-evolutionary framework for understanding and analysing corporate purpose, addressing its under-conceptualized nature and diverse interpretations in contemporary business studies. Grounded in the principles of coevolution and integrating Esposito De Falco’s (2012) framework on the genesis and evolution of firms, our work advances a unique theoretical typology of corporate purpose. This typology elucidates the evolutionary pathway of corporate purpose through the "3S" dimensions (structural, systemic, and strategic), offering organizations a guide to align their strategies with both their identity and public image. By examining corporate purpose through these dimensions, we highlight the interconnectedness of an organization’s intrinsic identity (structural), its engagement within its ecosystem (systemic), and the alignment of purpose with actionable strategies (strategic). This multi-dimensional approach reveals how corporate purpose guides firms through a transformative journey from their genesis to their interactions with stakeholders and broader market dynamics. To do this, the paper delineates four distinct typologies of corporate purpose: purpose washing, formal purpose, promising purpose, and deep purpose. These typologies are systematically organized within a synthesis matrix, providing a nuanced and detailed understanding of how corporate purpose manifests and evolves within different organizational contexts. Central to the study are two key propositions. The first proposition positions corporate purpose as the “metabolism of firms”, drawing a parallel with biological metabolism to underscore its crucial role in ensuring business survival, prosperity, and evolutionary adaptation. The second proposition emphasizes the importance of a sequential progression across the genetic, relational, and phenotypic stages of corporate purpose, which is critical for the design of genuinely purposeful organizations. By moving beyond a purely definitional approach, this paper contributes significantly to the ontological understanding of corporate purpose. It sheds light on the dynamics of purpose in organizations, highlighting its strategic importance and the need for alignment between a firm’s identity and actions. Our paper provides a valuable resource for academics and practitioners alike, seeking to navigate the complexities of corporate purpose in a dynamic business landscape. Keywords: Corporate purpose; evolutionary perspective; genotype; relational dimension; phenotype; corporate purpose typologies
Montera, Raffaella; Nevi, Giulia; Cucari, Nicola; Falco, Salvatore Esposito De
How firms adjust their SDG adoption in response to COVID-19 outbreak: a regional perspective Journal Article
In: Corporate Governance: The International Journal of Business in Society, vol. 24, no. 3, pp. 559–575, 2024, ISSN: 1472-0701, 1472-0701.
Abstract | Links | BibTeX | Tags: SDG, Strategy and Management
@article{montera_how_2024,
title = {How firms adjust their SDG adoption in response to COVID-19 outbreak: a regional perspective},
author = {Raffaella Montera and Giulia Nevi and Nicola Cucari and Salvatore Esposito De Falco},
url = {https://www.emerald.com/insight/content/doi/10.1108/CG-04-2023-0171/full/html},
doi = {10.1108/CG-04-2023-0171},
issn = {1472-0701, 1472-0701},
year = {2024},
date = {2024-04-01},
urldate = {2024-10-07},
journal = {Corporate Governance: The International Journal of Business in Society},
volume = {24},
number = {3},
pages = {559–575},
abstract = {Purpose
This paper aims to examine the COVID-19 pandemic’s impacts on the regional progression toward the Sustainable Development Goals (SDGs) through the lens of the adoption of 2030 Agenda by firms from different Italian regions.
Design/methodology/approach
Mixed methods were adopted. First, a content analysis was performed on 330 nonfinancial declarations released in the 2019–2021 period by a sample of 110 Italian listed companies from different regional macroareas. Second, regression analyses were run to test the impact of regional localization of businesses on SDGs adoption over pre-/during/post-COVID era.
Findings
The regional localization of businesses does not affect the SDGs adoption in the pre-COVID-19 era because Italian firms mainly address social goals. Instead, SDGs adoption is affected by regional localization of businesses both during and post-COVID-19 age, when Northern firms prioritize economic and social goals, whereas Southern firms shift from social to environmental goals.
Originality/value
This study fills the need of considering the subnational specificities in literature on sustainable development by capturing connections between firms, belonging territory, SDGs and COVID-19 crisis.},
keywords = {SDG, Strategy and Management},
pubstate = {published},
tppubtype = {article}
}
Purpose
This paper aims to examine the COVID-19 pandemic’s impacts on the regional progression toward the Sustainable Development Goals (SDGs) through the lens of the adoption of 2030 Agenda by firms from different Italian regions.
Design/methodology/approach
Mixed methods were adopted. First, a content analysis was performed on 330 nonfinancial declarations released in the 2019–2021 period by a sample of 110 Italian listed companies from different regional macroareas. Second, regression analyses were run to test the impact of regional localization of businesses on SDGs adoption over pre-/during/post-COVID era.
Findings
The regional localization of businesses does not affect the SDGs adoption in the pre-COVID-19 era because Italian firms mainly address social goals. Instead, SDGs adoption is affected by regional localization of businesses both during and post-COVID-19 age, when Northern firms prioritize economic and social goals, whereas Southern firms shift from social to environmental goals.
Originality/value
This study fills the need of considering the subnational specificities in literature on sustainable development by capturing connections between firms, belonging territory, SDGs and COVID-19 crisis.
This paper aims to examine the COVID-19 pandemic’s impacts on the regional progression toward the Sustainable Development Goals (SDGs) through the lens of the adoption of 2030 Agenda by firms from different Italian regions.
Design/methodology/approach
Mixed methods were adopted. First, a content analysis was performed on 330 nonfinancial declarations released in the 2019–2021 period by a sample of 110 Italian listed companies from different regional macroareas. Second, regression analyses were run to test the impact of regional localization of businesses on SDGs adoption over pre-/during/post-COVID era.
Findings
The regional localization of businesses does not affect the SDGs adoption in the pre-COVID-19 era because Italian firms mainly address social goals. Instead, SDGs adoption is affected by regional localization of businesses both during and post-COVID-19 age, when Northern firms prioritize economic and social goals, whereas Southern firms shift from social to environmental goals.
Originality/value
This study fills the need of considering the subnational specificities in literature on sustainable development by capturing connections between firms, belonging territory, SDGs and COVID-19 crisis.
Pontillo, Valeria; d'Aragona, Dario Amoroso; Pecorelli, Fabiano; Nucci, Dario Di; Ferrucci, Filomena; Palomba, Fabio
Machine learning-based test smell detection Journal Article
In: Empirical Software Engineering, vol. 29, no. 2, pp. 55, 2024, ISSN: 1382-3256, 1573-7616.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Code Smell Detection, Software Engineering, Technical Debt Management
@article{pontilloMachineLearningbasedTest2024,
title = {Machine learning-based test smell detection},
author = {Valeria Pontillo and Dario Amoroso d'Aragona and Fabiano Pecorelli and Dario Di Nucci and Filomena Ferrucci and Fabio Palomba},
url = {https://link.springer.com/10.1007/s10664-023-10436-2},
doi = {10.1007/s10664-023-10436-2},
issn = {1382-3256, 1573-7616},
year = {2024},
date = {2024-03-01},
urldate = {2024-07-07},
journal = {Empirical Software Engineering},
volume = {29},
number = {2},
pages = {55},
abstract = {Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques.},
keywords = {Artificial Intelligence, Code Smell Detection, Software Engineering, Technical Debt Management},
pubstate = {published},
tppubtype = {article}
}
Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques.
Lambiase, Stefano; Catolino, Gemma; Pecorelli, Fabiano; Tamburri, Damian A.; Palomba, Fabio; Heuvel, Willem-Jan Van Den; Ferrucci, Filomena
An Empirical Investigation Into the Influence of Software Communities' Cultural and Geographical Dispersion on Productivity Journal Article
In: Journal of Systems and Software, vol. 208, pp. 111878, 2024, ISSN: 01641212.
Abstract | Links | BibTeX | Tags: Culture Creativity and Inclusive Society, Empirical Software Engineering, Social Software Engineering, Strategy and Management
@article{lambiaseEmpiricalInvestigationInfluence2024,
title = {An Empirical Investigation Into the Influence of Software Communities' Cultural and Geographical Dispersion on Productivity},
author = {Stefano Lambiase and Gemma Catolino and Fabiano Pecorelli and Damian A. Tamburri and Fabio Palomba and Willem-Jan Van Den Heuvel and Filomena Ferrucci},
url = {https://linkinghub.elsevier.com/retrieve/pii/S016412122300273X},
doi = {10.1016/j.jss.2023.111878},
issn = {01641212},
year = {2024},
date = {2024-02-01},
urldate = {2024-07-07},
journal = {Journal of Systems and Software},
volume = {208},
pages = {111878},
abstract = {Estimating and understanding software development productivity represent crucial tasks for researchers and practitioners. Although different works focused on evaluating the impact of human factors on productivity, a few explored the influence of cultural/geographical diversity in software development communities. More particularly, all previous treatise addresses cultural aspects as abstract concepts without providing a quantitative representation. Improved knowledge of these matters might help project managers to assemble more productive teams and tool vendors to design software analytics toolkits that may better estimate productivity. This paper has the goal of enlarging the existing body of knowledge on the factors affecting productivity by focusing on cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it. To reach this goal, we performed a mixed-method empirical study. First, we built a statistical model relating dispersion metrics with the productivity of 25 open-source communities on Github. Then, we performed a confirmatory survey with 140 practitioners. The key results of our study indicate that cultural and geographical dispersion considerably impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle. We conclude our paper by elaborating on the main insights from our analyses and instilling implications that may drive further research.},
keywords = {Culture Creativity and Inclusive Society, Empirical Software Engineering, Social Software Engineering, Strategy and Management},
pubstate = {published},
tppubtype = {article}
}
Estimating and understanding software development productivity represent crucial tasks for researchers and practitioners. Although different works focused on evaluating the impact of human factors on productivity, a few explored the influence of cultural/geographical diversity in software development communities. More particularly, all previous treatise addresses cultural aspects as abstract concepts without providing a quantitative representation. Improved knowledge of these matters might help project managers to assemble more productive teams and tool vendors to design software analytics toolkits that may better estimate productivity. This paper has the goal of enlarging the existing body of knowledge on the factors affecting productivity by focusing on cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it. To reach this goal, we performed a mixed-method empirical study. First, we built a statistical model relating dispersion metrics with the productivity of 25 open-source communities on Github. Then, we performed a confirmatory survey with 140 practitioners. The key results of our study indicate that cultural and geographical dispersion considerably impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle. We conclude our paper by elaborating on the main insights from our analyses and instilling implications that may drive further research.
Montera, Raffaella; Falco, Salvatore Esposito De
Reaching the SDGs by 2030: At what point is Italy? Evidence from firms at the regional clusters’ level Journal Article
In: Sinergie Italian Journal of Management, vol. 42, no. 1, pp. 189–213, 2024, ISSN: 0393-5108, 2785-549X.
Abstract | Links | BibTeX | Tags: ESG, Strategy and Management, Sustainability
@article{montera_reaching_2024,
title = {Reaching the SDGs by 2030: At what point is Italy? Evidence from firms at the regional clusters’ level},
author = {Raffaella Montera and Salvatore Esposito De Falco},
url = {https://ojs.sijm.it/index.php/sinergie/workflow/index/1741/5},
doi = {10.7433/s123.2024.08},
issn = {0393-5108, 2785-549X},
year = {2024},
date = {2024-01-01},
urldate = {2024-10-07},
journal = {Sinergie Italian Journal of Management},
volume = {42},
number = {1},
pages = {189–213},
abstract = {Framing of the research. The implementation of the SDGs, one of the most urgent and current challenges, requires adaptation to sub-national contexts and the involvement of many actors, including firms. Purpose of the paper. The paper examines the Italian situation regarding the achievement of the SDGs through the lens of the adoption of the 2030 Agenda by firms from different Italian regions. Methodology. The research involved 30 Italian listed companies from Northern and Central-Southern Italy, selected from the CONSOB’s list of firms providing a non-financial declaration. An integral reading of the documents with subsequent interpretation was performed. Results. Regional localization does not affect the overall contribution to the SDGs, which is limited for all firms. Instead, the geographic localization of firms at the regional scale differentiates the prioritized SDGs: Northern firms are more oriented towards social and economic SDGs, while Central-Southern firms focus more on environmental ones. Research limitations. The paper represents a preliminary exploration of Italian firms’ advancements towards the SDGs over a regional space. Future research developments could focus on sample enlargement and the exploration of sub-national specificities in other countries around the world. Managerial implications. Italian firms should enhance their commitment to the 2030 Agenda in all its ambitions by incorporating the sustainable goals within their corporate culture and strategic posture. Originality of the paper. The study responds to the need to consider sub-national specificities in the literature on sustainable development by capturing the connections between firms, their territory of operation, and the SDGs.},
keywords = {ESG, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {article}
}
Framing of the research. The implementation of the SDGs, one of the most urgent and current challenges, requires adaptation to sub-national contexts and the involvement of many actors, including firms. Purpose of the paper. The paper examines the Italian situation regarding the achievement of the SDGs through the lens of the adoption of the 2030 Agenda by firms from different Italian regions. Methodology. The research involved 30 Italian listed companies from Northern and Central-Southern Italy, selected from the CONSOB’s list of firms providing a non-financial declaration. An integral reading of the documents with subsequent interpretation was performed. Results. Regional localization does not affect the overall contribution to the SDGs, which is limited for all firms. Instead, the geographic localization of firms at the regional scale differentiates the prioritized SDGs: Northern firms are more oriented towards social and economic SDGs, while Central-Southern firms focus more on environmental ones. Research limitations. The paper represents a preliminary exploration of Italian firms’ advancements towards the SDGs over a regional space. Future research developments could focus on sample enlargement and the exploration of sub-national specificities in other countries around the world. Managerial implications. Italian firms should enhance their commitment to the 2030 Agenda in all its ambitions by incorporating the sustainable goals within their corporate culture and strategic posture. Originality of the paper. The study responds to the need to consider sub-national specificities in the literature on sustainable development by capturing the connections between firms, their territory of operation, and the SDGs.
Montera, Raffaella
I controlli ispirati dall’Environmental (E), Social (S), Governance (G) Book Section
In: Corporate governance. Teorie, attori e sistemi di controllo nelle imprese, pp. 561–603, McGrow Hill, 2024, ISBN: 978-88-386-1219-0.
BibTeX | Tags: Corporate Governance, Strategy and Management, Sustainability
@incollection{montera_i_2024,
title = {I controlli ispirati dall’Environmental (E), Social (S), Governance (G)},
author = {Raffaella Montera},
isbn = {978-88-386-1219-0},
year = {2024},
date = {2024-01-01},
booktitle = {Corporate governance. Teorie, attori e sistemi di controllo nelle imprese},
pages = {561–603},
publisher = {McGrow Hill},
keywords = {Corporate Governance, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {incollection}
}
Montera, Raffaella
Il management e i suoi rapporti con la proprietà Book Section
In: Corporate governance. Teorie, attori e sistemi di controllo nelle imprese, pp. 329–370, McGrow Hill, 2024, ISBN: 978-88-386-1219-0.
BibTeX | Tags: Corporate Governance, Ownership, Strategy and Management
@incollection{montera_il_2024,
title = {Il management e i suoi rapporti con la proprietà},
author = {Raffaella Montera},
isbn = {978-88-386-1219-0},
year = {2024},
date = {2024-01-01},
booktitle = {Corporate governance. Teorie, attori e sistemi di controllo nelle imprese},
pages = {329–370},
publisher = {McGrow Hill},
keywords = {Corporate Governance, Ownership, Strategy and Management},
pubstate = {published},
tppubtype = {incollection}
}
Montera, Raffaella; Ciasullo, Maria Vincenza; Cucari, Nicola; Bianco, Rosario
The Customer Experience with Fashion Sale Robots: A Psycho-interpretative Framework Book Section
In: Ozuem, Wilson; Ranfagni, Silvia; Willis, Michelle (Ed.): Digital Transformation for Fashion and Luxury Brands, pp. 207–223, Springer International Publishing, Cham, 2024, ISBN: 978-3-031-35588-2 978-3-031-35589-9.
Abstract | Links | BibTeX | Tags: Management of Technology and Innovation, Strategy and Management
@incollection{ozuem_customer_2024,
title = {The Customer Experience with Fashion Sale Robots: A Psycho-interpretative Framework},
author = {Raffaella Montera and Maria Vincenza Ciasullo and Nicola Cucari and Rosario Bianco},
editor = {Wilson Ozuem and Silvia Ranfagni and Michelle Willis},
url = {https://link.springer.com/10.1007/978-3-031-35589-9_10},
doi = {10.1007/978-3-031-35589-9_10},
isbn = {978-3-031-35588-2 978-3-031-35589-9},
year = {2024},
date = {2024-01-01},
urldate = {2024-10-07},
booktitle = {Digital Transformation for Fashion and Luxury Brands},
pages = {207–223},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {This chapter aims to conceptually examine how perceptions of a customer experience with service robots are formed in a fashion retail context. The theoretical backbone is the integration of managerial and marketing view of human-machine interaction through Variety Information Model (VIM) and a psychological approach to the user adoption of new technologies according to Cognitive-Affective-Conative (CAC) model. The existing body of knowledge on humans’ reactions to service robots in a fashion retail context is enriched by proposing a new and multidisciplinary framework in which information units are the antecedents of customers’ experiences; synthesis schemes affect the cognitive experience; general schemes impact the conative experience; and categorical values are linked to the affective experience. The factors, from the customer’s side, conditioning the customer experience with fashion sale robots are thereby highlighted.},
keywords = {Management of Technology and Innovation, Strategy and Management},
pubstate = {published},
tppubtype = {incollection}
}
This chapter aims to conceptually examine how perceptions of a customer experience with service robots are formed in a fashion retail context. The theoretical backbone is the integration of managerial and marketing view of human-machine interaction through Variety Information Model (VIM) and a psychological approach to the user adoption of new technologies according to Cognitive-Affective-Conative (CAC) model. The existing body of knowledge on humans’ reactions to service robots in a fashion retail context is enriched by proposing a new and multidisciplinary framework in which information units are the antecedents of customers’ experiences; synthesis schemes affect the cognitive experience; general schemes impact the conative experience; and categorical values are linked to the affective experience. The factors, from the customer’s side, conditioning the customer experience with fashion sale robots are thereby highlighted.
Montera, Raffaella
Editorial: Sustainability, digitalization, performance: What reflections on corporate governance and organizational behavior? Journal Article
In: Corporate Governance and Organizational Behavior Review, vol. 8, no. 2, special issue, pp. 253–255, 2024, ISSN: 25211889, 25211870.
Abstract | Links | BibTeX | Tags: Organizational Behavior and Human Resource Management, Strategy and Management, Sustainability
@article{montera_editorial_2024,
title = {Editorial: Sustainability, digitalization, performance: What reflections on corporate governance and organizational behavior?},
author = {Raffaella Montera},
url = {https://virtusinterpress.org/Editorial-Sustainability-digitalization-performance-What-reflections-on-corporate-governance-and-organizational-behavior.html},
doi = {10.22495/cgobrv8i2sieditorial},
issn = {25211889, 25211870},
year = {2024},
date = {2024-01-01},
urldate = {2024-10-07},
journal = {Corporate Governance and Organizational Behavior Review},
volume = {8},
number = {2, special issue},
pages = {253–255},
abstract = {This issue contains thirteen papers, each of which contributes to a rich tapestry of contemporary research and practical insights. Amidst the diversity of topics covered by a variety of theoretical frameworks and methodological approaches, three key themes stand out: sustainability, innovation through digitalization, and people and organizational performance.},
keywords = {Organizational Behavior and Human Resource Management, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {article}
}
This issue contains thirteen papers, each of which contributes to a rich tapestry of contemporary research and practical insights. Amidst the diversity of topics covered by a variety of theoretical frameworks and methodological approaches, three key themes stand out: sustainability, innovation through digitalization, and people and organizational performance.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina
Forecasting the Developer’s Impact in Managing the Technical Debt Book Section
In: Kadgien, Regine; Jedlitschka, Andreas; Janes, Andrea; Lenarduzzi, Valentina; Li, Xiaozhou (Ed.): Product-Focused Software Process Improvement, vol. 14484, pp. 35–47, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-49268-6 978-3-031-49269-3.
Abstract | Links | BibTeX | Tags: Prediction, Technical Debt
@incollection{kadgien_forecasting_2024,
title = {Forecasting the Developer’s Impact in Managing the Technical Debt},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino},
editor = {Regine Kadgien and Andreas Jedlitschka and Andrea Janes and Valentina Lenarduzzi and Xiaozhou Li},
url = {https://link.springer.com/10.1007/978-3-031-49269-3_4},
doi = {10.1007/978-3-031-49269-3_4},
isbn = {978-3-031-49268-6 978-3-031-49269-3},
year = {2024},
date = {2024-01-01},
urldate = {2024-10-02},
booktitle = {Product-Focused Software Process Improvement},
volume = {14484},
pages = {35–47},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Technical debt is a collection of design decisions that, when taken together over time, make the system challenging to maintain and develop. Technical debt impacts the quality of applications by generating structural weaknesses that translate into slowness and functional deficiencies at the development level. Identifying debts in your code, architecture, and infrastructure is of paramount importance and requires an in-depth analysis that requires effort in terms of time and resources. To date, there are several reliable tools for calculating debt in code, but this study aims to forecast the impact developers have on debt in source code. We propose an approach, based on the use of different Machine Learning and Deep Learning classifiers capable of predicting just in time, if the change that the developer is making will have a low, medium, or high impact on the debt. To conduct the experiments, three open-source Java systems available on Github were selected, and for each of these, the entire history was collected in terms of changes, quality metrics and indicators strictly connected to the presence of technical debt. The results obtained are satisfactory, showing the effectiveness of the proposed method.},
keywords = {Prediction, Technical Debt},
pubstate = {published},
tppubtype = {incollection}
}
Technical debt is a collection of design decisions that, when taken together over time, make the system challenging to maintain and develop. Technical debt impacts the quality of applications by generating structural weaknesses that translate into slowness and functional deficiencies at the development level. Identifying debts in your code, architecture, and infrastructure is of paramount importance and requires an in-depth analysis that requires effort in terms of time and resources. To date, there are several reliable tools for calculating debt in code, but this study aims to forecast the impact developers have on debt in source code. We propose an approach, based on the use of different Machine Learning and Deep Learning classifiers capable of predicting just in time, if the change that the developer is making will have a low, medium, or high impact on the debt. To conduct the experiments, three open-source Java systems available on Github were selected, and for each of these, the entire history was collected in terms of changes, quality metrics and indicators strictly connected to the presence of technical debt. The results obtained are satisfactory, showing the effectiveness of the proposed method.
2023
Gigante, Domenico; Pecorelli, Fabiano; Barletta, Vita Santa; Janes, Andrea; Lenarduzzi, Valentina; Taibi, Davide; Baldassarre, Maria Teresa
Resolving Security Issues via Quality-Oriented Refactoring: A User Study Proceedings Article
In: 2023 ACM/IEEE International Conference on Technical Debt (TechDebt), pp. 82–91, IEEE, Melbourne, Australia, 2023, ISBN: 9798350311945.
Abstract | Links | BibTeX | Tags: Empirical Software Engineering, Software, Technical Debt Management
@inproceedings{giganteResolvingSecurityIssues2023,
title = {Resolving Security Issues via Quality-Oriented Refactoring: A User Study},
author = {Domenico Gigante and Fabiano Pecorelli and Vita Santa Barletta and Andrea Janes and Valentina Lenarduzzi and Davide Taibi and Maria Teresa Baldassarre},
url = {https://ieeexplore.ieee.org/document/10207139/},
doi = {10.1109/TechDebt59074.2023.00016},
isbn = {9798350311945},
year = {2023},
date = {2023-05-01},
urldate = {2024-07-07},
booktitle = {2023 ACM/IEEE International Conference on Technical Debt (TechDebt)},
pages = {82–91},
publisher = {IEEE},
address = {Melbourne, Australia},
abstract = {Software quality is crucial in software development: if not addressed in early phases of the software development life cycle, it may even lead to technical bankruptcy, i.e., a situation in which modifications cost more than redeveloping the application from scratch. In addition, code security must also be addressed to reduce software vulnerabilities and to comply with legal requirements. In this work, we aim to investigate the relationship between refactoring code quality and software security, with the purpose of understanding whether and to what extent improving software quality could have a positive impact on software security as well. Specifically, we investigate to what extent rule violations of a software quality tool such as SonarQube overlap with rule violations of a software vulnerability tool like Fortify Static Code Analyzer. We first compared the rules encoded in the quality models of both tools, to discover possible overlapping cases. Later, we compared the issues raised by both tools on a set of open source Java projects; we also investigated the cases in which a quality refactoring process impacts over software security (thus removing one or more vulnerabilities). We furthermore validated our results statistically. Our results show that resolving software quality issues might also resolve security issues but only in part: many security issues still persist in the source code; also, some quality aspects are more likely to be improved in respect to others. In addition, this empirical study uncovers rule co-occurrences between the two tools. This study confirms the need for using a security-oriented static analysis tool to enforce software security instead of relying only on a quality-oriented one. Results have highlighted important insights for practitioners.},
keywords = {Empirical Software Engineering, Software, Technical Debt Management},
pubstate = {published},
tppubtype = {inproceedings}
}
Software quality is crucial in software development: if not addressed in early phases of the software development life cycle, it may even lead to technical bankruptcy, i.e., a situation in which modifications cost more than redeveloping the application from scratch. In addition, code security must also be addressed to reduce software vulnerabilities and to comply with legal requirements. In this work, we aim to investigate the relationship between refactoring code quality and software security, with the purpose of understanding whether and to what extent improving software quality could have a positive impact on software security as well. Specifically, we investigate to what extent rule violations of a software quality tool such as SonarQube overlap with rule violations of a software vulnerability tool like Fortify Static Code Analyzer. We first compared the rules encoded in the quality models of both tools, to discover possible overlapping cases. Later, we compared the issues raised by both tools on a set of open source Java projects; we also investigated the cases in which a quality refactoring process impacts over software security (thus removing one or more vulnerabilities). We furthermore validated our results statistically. Our results show that resolving software quality issues might also resolve security issues but only in part: many security issues still persist in the source code; also, some quality aspects are more likely to be improved in respect to others. In addition, this empirical study uncovers rule co-occurrences between the two tools. This study confirms the need for using a security-oriented static analysis tool to enforce software security instead of relying only on a quality-oriented one. Results have highlighted important insights for practitioners.
Lenarduzzi, Valentina; Pecorelli, Fabiano; Saarimaki, Nyyti; Lujan, Savanna; Palomba, Fabio
A critical comparison on six static analysis tools: Detection, agreement, and precision Journal Article
In: Journal of Systems and Software, vol. 198, pp. 111575, 2023, ISSN: 01641212.
Abstract | Links | BibTeX | Tags: Empirical Software Engineering, Software, Technical Debt Management
@article{lenarduzziCriticalComparisonSix2023,
title = {A critical comparison on six static analysis tools: Detection, agreement, and precision},
author = {Valentina Lenarduzzi and Fabiano Pecorelli and Nyyti Saarimaki and Savanna Lujan and Fabio Palomba},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121222002515},
doi = {10.1016/j.jss.2022.111575},
issn = {01641212},
year = {2023},
date = {2023-04-01},
urldate = {2024-07-07},
journal = {Journal of Systems and Software},
volume = {198},
pages = {111575},
abstract = {Background: Developers use Static Analysis Tools (SATs) to control for potential quality issues in source code, including defects and technical debt. Tool vendors have devised quite a number of tools, which makes it harder for practitioners to select the most suitable one for their needs. To better support developers, researchers have been conducting several studies on SATs to favor the understanding of their actual capabilities. Aims: Despite the work done so far, there is still a lack of knowledge regarding (1) what is their agreement, and (2) what is the precision of their recommendations. We aim at bridging this gap by proposing a large-scale comparison of six popular SATs for Java projects: Better Code Hub, CheckStyle, Coverity Scan, FindBugs, PMD, and SonarQube. Methods: We analyze 47 Java projects applying 6 SATs. To assess their agreement, we compared them by manually analyzing – at line – and class-level — whether they identify the same issues. Finally, we evaluate the precision of the tools against a manually-defined ground truth. Results: The key results show little to no agreement among the tools and a low degree of precision. Conclusion: Our study provides the first overview on the agreement among different tools as well as an extensive analysis of their precision that can be used by researchers, practitioners, and tool vendors to map the current capabilities of the tools and envision possible improvements.},
keywords = {Empirical Software Engineering, Software, Technical Debt Management},
pubstate = {published},
tppubtype = {article}
}
Background: Developers use Static Analysis Tools (SATs) to control for potential quality issues in source code, including defects and technical debt. Tool vendors have devised quite a number of tools, which makes it harder for practitioners to select the most suitable one for their needs. To better support developers, researchers have been conducting several studies on SATs to favor the understanding of their actual capabilities. Aims: Despite the work done so far, there is still a lack of knowledge regarding (1) what is their agreement, and (2) what is the precision of their recommendations. We aim at bridging this gap by proposing a large-scale comparison of six popular SATs for Java projects: Better Code Hub, CheckStyle, Coverity Scan, FindBugs, PMD, and SonarQube. Methods: We analyze 47 Java projects applying 6 SATs. To assess their agreement, we compared them by manually analyzing – at line – and class-level — whether they identify the same issues. Finally, we evaluate the precision of the tools against a manually-defined ground truth. Results: The key results show little to no agreement among the tools and a low degree of precision. Conclusion: Our study provides the first overview on the agreement among different tools as well as an extensive analysis of their precision that can be used by researchers, practitioners, and tool vendors to map the current capabilities of the tools and envision possible improvements.
d'Aragona, Dario Amoroso; Pecorelli, Fabiano; Baldassarre, Maria Teresa; Taibi, Davide; Lenarduzzi, Valentina
Technical Debt Diffuseness in the Apache Ecosystem: A Differentiated Replication Proceedings Article
In: 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 825–833, IEEE, Taipa, Macao, 2023, ISBN: 978-1-66545-278-6.
Abstract | Links | BibTeX | Tags: Software, Software Engineering, Technical Debt Management
@inproceedings{daragonaTechnicalDebtDiffuseness2023,
title = {Technical Debt Diffuseness in the Apache Ecosystem: A Differentiated Replication},
author = {Dario Amoroso d'Aragona and Fabiano Pecorelli and Maria Teresa Baldassarre and Davide Taibi and Valentina Lenarduzzi},
url = {https://ieeexplore.ieee.org/document/10123641/},
doi = {10.1109/SANER56733.2023.00095},
isbn = {978-1-66545-278-6},
year = {2023},
date = {2023-03-01},
urldate = {2024-07-07},
booktitle = {2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)},
pages = {825–833},
publisher = {IEEE},
address = {Taipa, Macao},
abstract = {Technical debt management is a critical activity that is gaining the attention of both practitioners and researchers. Several tools providing automatic support for technical debt management have been introduced over the last years. SonarQube is one of the most widely applied tools to automatically measure technical debt in software systems. SonarQube has been adopted to quantify the diffuseness of technical debt in projects of the Apache Software Foundation ecosystem. Lenarduzzi et al. [1] found that the vast majority of technical debt issues in the code are code smells and that, surprisingly, developers tend to take more time to remove severe issues than the less-severe ones. While this study provides very interesting insights both for researchers and practitioners interested in technical debt management, we identified some major limitations that could have led to results that do not perfectly reflect reality. This study aims to address such limitations by presenting a differentiated replication study. Our findings have pointed out significant differences with the reference work. The results show that technical debt issues appear much more rarely than what the reference work reported.In this study, we implemented a new methodology to calculate the diffuseness of SonarQube issues at project and commit level, based on the reconstruction of the SonarQube quality profile in order to understand how the quality profile has evolved and to compare the number of active rules per category and severity level with the respective number of issues found. The results show that over 50% of rules active in the quality profile, are Code Smell rules and that over 90% of the issues belong to Code Smell category. Furthermore, analyzing the life span of the issues, we found that developers take into account the level of severity of the issues only for the Bug category, thus fixing the issues starting from the most severe, which is not the case for the other categories.},
keywords = {Software, Software Engineering, Technical Debt Management},
pubstate = {published},
tppubtype = {inproceedings}
}
Technical debt management is a critical activity that is gaining the attention of both practitioners and researchers. Several tools providing automatic support for technical debt management have been introduced over the last years. SonarQube is one of the most widely applied tools to automatically measure technical debt in software systems. SonarQube has been adopted to quantify the diffuseness of technical debt in projects of the Apache Software Foundation ecosystem. Lenarduzzi et al. [1] found that the vast majority of technical debt issues in the code are code smells and that, surprisingly, developers tend to take more time to remove severe issues than the less-severe ones. While this study provides very interesting insights both for researchers and practitioners interested in technical debt management, we identified some major limitations that could have led to results that do not perfectly reflect reality. This study aims to address such limitations by presenting a differentiated replication study. Our findings have pointed out significant differences with the reference work. The results show that technical debt issues appear much more rarely than what the reference work reported.In this study, we implemented a new methodology to calculate the diffuseness of SonarQube issues at project and commit level, based on the reconstruction of the SonarQube quality profile in order to understand how the quality profile has evolved and to compare the number of active rules per category and severity level with the respective number of issues found. The results show that over 50% of rules active in the quality profile, are Code Smell rules and that over 90% of the issues belong to Code Smell category. Furthermore, analyzing the life span of the issues, we found that developers take into account the level of severity of the issues only for the Bug category, thus fixing the issues starting from the most severe, which is not the case for the other categories.
Ciasullo, Maria Vincenza; Montera, Raffaella; Ferrara, Miriana
Digital Readiness and Resilience of Digitally Servitized Firms: A Business Model Innovation Perspective Book Section
In: Visvizi, Anna; Troisi, Orlando; Grimaldi, Mara (Ed.): Research and Innovation Forum 2022, pp. 509–517, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-19559-4 978-3-031-19560-0, (Series Title: Springer Proceedings in Complexity).
Links | BibTeX | Tags: Digital Servitization, Strategy and Management
@incollection{visvizi_digital_2023,
title = {Digital Readiness and Resilience of Digitally Servitized Firms: A Business Model Innovation Perspective},
author = {Maria Vincenza Ciasullo and Raffaella Montera and Miriana Ferrara},
editor = {Anna Visvizi and Orlando Troisi and Mara Grimaldi},
url = {https://link.springer.com/10.1007/978-3-031-19560-0_42},
doi = {10.1007/978-3-031-19560-0_42},
isbn = {978-3-031-19559-4 978-3-031-19560-0},
year = {2023},
date = {2023-01-01},
urldate = {2024-10-07},
booktitle = {Research and Innovation Forum 2022},
pages = {509–517},
publisher = {Springer International Publishing},
address = {Cham},
note = {Series Title: Springer Proceedings in Complexity},
keywords = {Digital Servitization, Strategy and Management},
pubstate = {published},
tppubtype = {incollection}
}
Falco, Salvatore Esposito De; Montera, Raffaella; Leo, Sabrina; Vito, Pietro; Sardanelli, Domenico; Laviola, Francesco; Alaia, Raffaele; Nevi, Giulia
Mapping ESG's studies in business management field: A review based on bibliographic coupling analysis Book Section
In: EURAM 2023 Management Conference Proceedings, 2023, ISBN: 978-2-9602195-5-5.
Abstract | BibTeX | Tags: ESG, Strategy and Management, Sustainability
@incollection{de_falco_mapping_2023,
title = {Mapping ESG's studies in business management field: A review based on bibliographic coupling analysis},
author = {Salvatore Esposito De Falco and Raffaella Montera and Sabrina Leo and Pietro Vito and Domenico Sardanelli and Francesco Laviola and Raffaele Alaia and Giulia Nevi},
isbn = {978-2-9602195-5-5},
year = {2023},
date = {2023-01-01},
booktitle = {EURAM 2023 Management Conference Proceedings},
abstract = {Profound changes in the economic scenario are occurring. One of these is the increase in demand for and interest in Environmental social governance (ESG) aspects that have led firms to fundamentally rethink and reshape their business model, priorities, and purposes. The ESG narrative is that it is now recognized as a key determinant of future value creation. However, no review has explored the ESG literature by applying a bibliographic coupling analysis, especially to capture current and future research trends of ESG within a business management field. Therefore, the objectives of this review are twofold: i) to identify and examine in-depth clusters representing the latest research themes in the ESG field; ii) to propose insights into avenues for further research through a review of the articles included within each thematic cluster. We achieve these objectives by conducting a bibliographic coupling to provide a prospective approach, utilizing VOSviewer software Based on an analysis of 903 papers, 4 clusters were identified. In addition, a qualitative approach was made to complete the bibliographic coupling analysis. Therefore, all articles included in each cluster were read with the aim of identifying their most outstanding contributions, as well as the main research methods and theoretical perspectives employed. Finally, the paper offers potential new areas for further exploration.},
keywords = {ESG, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {incollection}
}
Profound changes in the economic scenario are occurring. One of these is the increase in demand for and interest in Environmental social governance (ESG) aspects that have led firms to fundamentally rethink and reshape their business model, priorities, and purposes. The ESG narrative is that it is now recognized as a key determinant of future value creation. However, no review has explored the ESG literature by applying a bibliographic coupling analysis, especially to capture current and future research trends of ESG within a business management field. Therefore, the objectives of this review are twofold: i) to identify and examine in-depth clusters representing the latest research themes in the ESG field; ii) to propose insights into avenues for further research through a review of the articles included within each thematic cluster. We achieve these objectives by conducting a bibliographic coupling to provide a prospective approach, utilizing VOSviewer software Based on an analysis of 903 papers, 4 clusters were identified. In addition, a qualitative approach was made to complete the bibliographic coupling analysis. Therefore, all articles included in each cluster were read with the aim of identifying their most outstanding contributions, as well as the main research methods and theoretical perspectives employed. Finally, the paper offers potential new areas for further exploration.
Cucari, Nicola; Tutore, Ilaria; Montera, Raffaella; Profita, Sofia
A bibliometric performance analysis of publication productivity in the corporate social responsibility field: Outcomes of SciVal analytics Journal Article
In: Corporate Social Responsibility and Environmental Management, vol. 30, no. 1, pp. 1–16, 2023, ISSN: 1535-3958, 1535-3966.
Abstract | Links | BibTeX | Tags: Bibliometric Performance Analysis, Corporate Social Responsibility, Strategy and Management
@article{cucari_bibliometric_2023,
title = {A bibliometric performance analysis of publication productivity in the corporate social responsibility field: Outcomes of SciVal analytics},
author = {Nicola Cucari and Ilaria Tutore and Raffaella Montera and Sofia Profita},
url = {https://onlinelibrary.wiley.com/doi/10.1002/csr.2346},
doi = {10.1002/csr.2346},
issn = {1535-3958, 1535-3966},
year = {2023},
date = {2023-01-01},
urldate = {2024-10-07},
journal = {Corporate Social Responsibility and Environmental Management},
volume = {30},
number = {1},
pages = {1–16},
abstract = {Corporate social responsibility (CSR) research in academia has been increasing over time, especially in recent years. Thus, the evaluation of relative research productivity is becoming increasingly relevant due to the current importance of this topic. The paper aims to investigate research productivity in the CSR field by performing a bibliometric analysis. An iterative search strategy was used to firstly identify productive CSR authors and then examine their productivity over a 5‐year period (2015–2020) using the SciVal tool by Elsevier. Drawing on the results from different bibliometric analyses, the study investigates quantitative and qualitative publication performance at the country (macro), institutional (meso) and individual (micro) levels. This study is the first bibliometric analysis on CSR that is not related to a specific journal and has a multi‐level nature by providing the groundwork in determining the knowledge structure of CSR.},
keywords = {Bibliometric Performance Analysis, Corporate Social Responsibility, Strategy and Management},
pubstate = {published},
tppubtype = {article}
}
Corporate social responsibility (CSR) research in academia has been increasing over time, especially in recent years. Thus, the evaluation of relative research productivity is becoming increasingly relevant due to the current importance of this topic. The paper aims to investigate research productivity in the CSR field by performing a bibliometric analysis. An iterative search strategy was used to firstly identify productive CSR authors and then examine their productivity over a 5‐year period (2015–2020) using the SciVal tool by Elsevier. Drawing on the results from different bibliometric analyses, the study investigates quantitative and qualitative publication performance at the country (macro), institutional (meso) and individual (micro) levels. This study is the first bibliometric analysis on CSR that is not related to a specific journal and has a multi‐level nature by providing the groundwork in determining the knowledge structure of CSR.
2022
Ardimento, Pasquale; Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina
Using deep temporal convolutional networks to just-in-time forecast technical debt principal Journal Article
In: Journal of Systems and Software, vol. 194, pp. 111481, 2022, ISSN: 01641212.
Abstract | Links | BibTeX | Tags: Technical Debt
@article{ardimento_using_2022,
title = {Using deep temporal convolutional networks to just-in-time forecast technical debt principal},
author = {Pasquale Ardimento and Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121222001649},
doi = {10.1016/j.jss.2022.111481},
issn = {01641212},
year = {2022},
date = {2022-12-01},
urldate = {2024-10-02},
journal = {Journal of Systems and Software},
volume = {194},
pages = {111481},
abstract = {Technical debt is a widely used metaphor to summarize all the consequences of poorly written code. Managing technical debt is important for software developers to allow adequate planning for software maintenance and improvement activities, such as refactoring and preventing system degradation. Several studies in the literature investigate the identification of technical debt and its consequences. This work aims to explore a deep learning approach to just-in-time predict the impact on technical debt when changes are performed on the source code. In this way the developer can work better, trying to improve the quality of the code that is being modified. Knowing what the TD trend will be in just-in-time source code with the change made is the key to avoiding a project taking a long time to remediate or improve. The model exploits the knowledge of quality and ad-hoc process metrics evolution over time. To validate the approach, a large dataset, including metrics evaluated from commits of ten Java software projects, was built. The results obtained show the effectiveness of the proposed approach in predicting the Technical Debt accumulation within the source code.},
keywords = {Technical Debt},
pubstate = {published},
tppubtype = {article}
}
Technical debt is a widely used metaphor to summarize all the consequences of poorly written code. Managing technical debt is important for software developers to allow adequate planning for software maintenance and improvement activities, such as refactoring and preventing system degradation. Several studies in the literature investigate the identification of technical debt and its consequences. This work aims to explore a deep learning approach to just-in-time predict the impact on technical debt when changes are performed on the source code. In this way the developer can work better, trying to improve the quality of the code that is being modified. Knowing what the TD trend will be in just-in-time source code with the change made is the key to avoiding a project taking a long time to remediate or improve. The model exploits the knowledge of quality and ad-hoc process metrics evolution over time. To validate the approach, a large dataset, including metrics evaluated from commits of ten Java software projects, was built. The results obtained show the effectiveness of the proposed approach in predicting the Technical Debt accumulation within the source code.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina; Montano, Debora
Forecasting technical debt evolution in software systems: an empirical study Journal Article
In: Frontiers of Computer Science, vol. 17, no. 3, pp. 173210, 2022, ISSN: 2095-2236.
Abstract | Links | BibTeX | Tags: Empirical Study, Machine Learning, Software Quality, Technical Debt
@article{aversano_forecasting_2022,
title = {Forecasting technical debt evolution in software systems: an empirical study},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Debora Montano},
url = {https://doi.org/10.1007/s11704-022-1541-7},
doi = {10.1007/s11704-022-1541-7},
issn = {2095-2236},
year = {2022},
date = {2022-11-01},
urldate = {2024-10-02},
journal = {Frontiers of Computer Science},
volume = {17},
number = {3},
pages = {173210},
abstract = {Technical debt is considered detrimental to the long-term success of software development, but despite the numerous studies in the literature, there are still many aspects that need to be investigated for a better understanding of it. In particular, the main problems that hinder its complete understanding are the absence of a clear definition and a model for its identification, management, and forecasting. Focusing on forecasting technical debt, there is a growing notion that preventing technical debt build-up allows you to identify and address the riskiest debt items for the project before they can permanently compromise it. However, despite this high relevance, the forecast of technical debt is still little explored. To this end, this study aims to evaluate whether the quality metrics of a software system can be useful for the correct prediction of the technical debt. Therefore, the data related to the quality metrics of 8 different open-source software systems were analyzed and supplied as input to multiple machine learning algorithms to perform the prediction of the technical debt. In addition, several partitions of the initial dataset were evaluated to assess whether prediction performance could be improved by performing a data selection. The results obtained show good forecasting performance and the proposed document provides a useful approach to understanding the overall phenomenon of technical debt for practical purposes.},
keywords = {Empirical Study, Machine Learning, Software Quality, Technical Debt},
pubstate = {published},
tppubtype = {article}
}
Technical debt is considered detrimental to the long-term success of software development, but despite the numerous studies in the literature, there are still many aspects that need to be investigated for a better understanding of it. In particular, the main problems that hinder its complete understanding are the absence of a clear definition and a model for its identification, management, and forecasting. Focusing on forecasting technical debt, there is a growing notion that preventing technical debt build-up allows you to identify and address the riskiest debt items for the project before they can permanently compromise it. However, despite this high relevance, the forecast of technical debt is still little explored. To this end, this study aims to evaluate whether the quality metrics of a software system can be useful for the correct prediction of the technical debt. Therefore, the data related to the quality metrics of 8 different open-source software systems were analyzed and supplied as input to multiple machine learning algorithms to perform the prediction of the technical debt. In addition, several partitions of the initial dataset were evaluated to assess whether prediction performance could be improved by performing a data selection. The results obtained show good forecasting performance and the proposed document provides a useful approach to understanding the overall phenomenon of technical debt for practical purposes.
Stefano, Manuel De; Pecorelli, Fabiano; Nucci, Dario Di; Lucia, Andrea De
A preliminary evaluation on the relationship among architectural and test smells Proceedings Article
In: 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM), pp. 66–70, IEEE, Limassol, Cyprus, 2022, ISBN: 978-1-66549-609-4.
Abstract | Links | BibTeX | Tags: Empirical Software Engineering, Software, Technical Debt Management
@inproceedings{destefanoPreliminaryEvaluationRelationship2022,
title = {A preliminary evaluation on the relationship among architectural and test smells},
author = {Manuel De Stefano and Fabiano Pecorelli and Dario Di Nucci and Andrea De Lucia},
url = {https://ieeexplore.ieee.org/document/10006864/},
doi = {10.1109/SCAM55253.2022.00013},
isbn = {978-1-66549-609-4},
year = {2022},
date = {2022-10-01},
urldate = {2024-07-07},
booktitle = {2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM)},
pages = {66–70},
publisher = {IEEE},
address = {Limassol, Cyprus},
abstract = {Software maintenance is the software life cycle's longest and most challenging phase. Bad architectural decisions or sub-optimal solutions might lead to architectural erosion, i.e., the process that causes the system's architecture to deviate from its original design. The so-called architectural smells are the most common signs of architectural erosion. Architectural smells might affect several quality aspects of a software system, including testability. When a system is not prone to testing, sub-optimal solutions may be introduced in the test code, a.k.a. test smells. This paper explores the possible relations between architectural and test smells. By mining 798 releases of 40 open-source Java systems, we studied the correlation between class-level architectural and test smells. In particular, Eager Test and Assertion Roulette smells often occur in conjunction with Cyclically-dependent Modularization, Deficient Encapsulation, and Insufficient Encapsulation architectural smells.},
keywords = {Empirical Software Engineering, Software, Technical Debt Management},
pubstate = {published},
tppubtype = {inproceedings}
}
Software maintenance is the software life cycle's longest and most challenging phase. Bad architectural decisions or sub-optimal solutions might lead to architectural erosion, i.e., the process that causes the system's architecture to deviate from its original design. The so-called architectural smells are the most common signs of architectural erosion. Architectural smells might affect several quality aspects of a software system, including testability. When a system is not prone to testing, sub-optimal solutions may be introduced in the test code, a.k.a. test smells. This paper explores the possible relations between architectural and test smells. By mining 798 releases of 40 open-source Java systems, we studied the correlation between class-level architectural and test smells. In particular, Eager Test and Assertion Roulette smells often occur in conjunction with Cyclically-dependent Modularization, Deficient Encapsulation, and Insufficient Encapsulation architectural smells.
Ciasullo, Maria V.; Montera, Raffaella; Douglas, Alexander
Environmental sustainability orientation and ambidextrous green innovation: do the roles of women on corporate boards matter? Journal Article
In: Sinergie Italian Journal of Management, vol. 40, no. 2, pp. 209–231, 2022, ISSN: 0393-5108, 2785-549X.
Abstract | Links | BibTeX | Tags: Corporate Governance, Strategy and Management, Sustainability
@article{ciasullo_environmental_2022,
title = {Environmental sustainability orientation and ambidextrous green innovation: do the roles of women on corporate boards matter?},
author = {Maria V. Ciasullo and Raffaella Montera and Alexander Douglas},
url = {https://www.sijm.it/wp-content/uploads/2022/09/10-ciasullo-et-al.-118-2022.pdf},
doi = {10.7433/s118.2022.10},
issn = {0393-5108, 2785-549X},
year = {2022},
date = {2022-08-01},
urldate = {2024-10-07},
journal = {Sinergie Italian Journal of Management},
volume = {40},
number = {2},
pages = {209–231},
abstract = {Purpose of the paper: This study investigates the relationship between environmental sustainability orientation and green innovation ambidexterity and considers the role of women on corporate boards in moderating this relationship. To this end, a research model was developed according to the Natural Resource Based View Theory and the Upper Echelon Theory.
Methodology: A survey was conducted of 116 listed Italian companies with at least one female director holding a specified role. A moderated hierarchical regression was employed to test the research hypotheses.
Findings: Environmental sustainability orientation positively influences green exploitation innovation and exploration innovation. Moreover, the roles of women on corporate boards strengthen the effect of environmental sustainability orientation on green innovation ambidexterity.
Research limits: The size of the sample and its nature required prudence in generalizing the results to unlisted Italian companies.
Practical implications: Environmental sustainability orientation is an affordable solution to address the innovation paradox, since it contributes to a balance between exploitation and exploration activities. Moreover, a higher number of female directors should be present on the boards of firms that look to improve environmental sustainability by fostering green innovation ambidexterity.
Originality of the paper: Environmental sustainability orientation, green innovation ambidexterity, and women on corporate boards were contextually investigated for the first time. Thus, the study adds a more granular understanding of these constructs through a model that is theoretically derived and empirically examined.},
keywords = {Corporate Governance, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {article}
}
Purpose of the paper: This study investigates the relationship between environmental sustainability orientation and green innovation ambidexterity and considers the role of women on corporate boards in moderating this relationship. To this end, a research model was developed according to the Natural Resource Based View Theory and the Upper Echelon Theory.
Methodology: A survey was conducted of 116 listed Italian companies with at least one female director holding a specified role. A moderated hierarchical regression was employed to test the research hypotheses.
Findings: Environmental sustainability orientation positively influences green exploitation innovation and exploration innovation. Moreover, the roles of women on corporate boards strengthen the effect of environmental sustainability orientation on green innovation ambidexterity.
Research limits: The size of the sample and its nature required prudence in generalizing the results to unlisted Italian companies.
Practical implications: Environmental sustainability orientation is an affordable solution to address the innovation paradox, since it contributes to a balance between exploitation and exploration activities. Moreover, a higher number of female directors should be present on the boards of firms that look to improve environmental sustainability by fostering green innovation ambidexterity.
Originality of the paper: Environmental sustainability orientation, green innovation ambidexterity, and women on corporate boards were contextually investigated for the first time. Thus, the study adds a more granular understanding of these constructs through a model that is theoretically derived and empirically examined.
Methodology: A survey was conducted of 116 listed Italian companies with at least one female director holding a specified role. A moderated hierarchical regression was employed to test the research hypotheses.
Findings: Environmental sustainability orientation positively influences green exploitation innovation and exploration innovation. Moreover, the roles of women on corporate boards strengthen the effect of environmental sustainability orientation on green innovation ambidexterity.
Research limits: The size of the sample and its nature required prudence in generalizing the results to unlisted Italian companies.
Practical implications: Environmental sustainability orientation is an affordable solution to address the innovation paradox, since it contributes to a balance between exploitation and exploration activities. Moreover, a higher number of female directors should be present on the boards of firms that look to improve environmental sustainability by fostering green innovation ambidexterity.
Originality of the paper: Environmental sustainability orientation, green innovation ambidexterity, and women on corporate boards were contextually investigated for the first time. Thus, the study adds a more granular understanding of these constructs through a model that is theoretically derived and empirically examined.
Lambiase, Stefano; Catolino, Gemma; Pecorelli, Fabiano; Tamburri, Damian A.; Palomba, Fabio; Heuvel, Willem-Jan Van Den; Ferrucci, Filomena
“There and Back Again?” On the Influence of Software Community Dispersion Over Productivity Proceedings Article
In: 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 177–184, IEEE, Gran Canaria, Spain, 2022, ISBN: 978-1-66546-152-8.
Abstract | Links | BibTeX | Tags: Culture Creativity and Inclusive Society, Empirical Software Engineering, Social Software Engineering, Strategy and Management
@inproceedings{lambiaseThereBackAgain2022,
title = {“There and Back Again?” On the Influence of Software Community Dispersion Over Productivity},
author = {Stefano Lambiase and Gemma Catolino and Fabiano Pecorelli and Damian A. Tamburri and Fabio Palomba and Willem-Jan Van Den Heuvel and Filomena Ferrucci},
url = {https://ieeexplore.ieee.org/document/10011201/},
doi = {10.1109/SEAA56994.2022.00035},
isbn = {978-1-66546-152-8},
year = {2022},
date = {2022-08-01},
urldate = {2024-07-07},
booktitle = {2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)},
pages = {177–184},
publisher = {IEEE},
address = {Gran Canaria, Spain},
abstract = {Estimating and understanding productivity still represents a crucial task for researchers and practitioners. Researchers spent significant effort identifying the factors that influence software developers' productivity, providing several approaches for analyzing and predicting such a metric. Although different works focused on evaluating the impact of human factors on productivity, little is known about the influence of cultural/geographical diversity in software development communities. Indeed, in previous studies, researchers treated cultural aspects like an abstract concept without providing a quantitative representation. This work provides an empirical assessment of the relationship between cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it—and its productivity. To reach our aim, we built a statistical model that contained product and socio-technical factors as independent variables to assess the correlation with productivity, i.e., the number of commits performed in a given time. Then, we ran our model considering data of 25 open-source communities on GitHub. Results of our study indicate that cultural and geographical dispersion impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle.},
keywords = {Culture Creativity and Inclusive Society, Empirical Software Engineering, Social Software Engineering, Strategy and Management},
pubstate = {published},
tppubtype = {inproceedings}
}
Estimating and understanding productivity still represents a crucial task for researchers and practitioners. Researchers spent significant effort identifying the factors that influence software developers' productivity, providing several approaches for analyzing and predicting such a metric. Although different works focused on evaluating the impact of human factors on productivity, little is known about the influence of cultural/geographical diversity in software development communities. Indeed, in previous studies, researchers treated cultural aspects like an abstract concept without providing a quantitative representation. This work provides an empirical assessment of the relationship between cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it—and its productivity. To reach our aim, we built a statistical model that contained product and socio-technical factors as independent variables to assess the correlation with productivity, i.e., the number of commits performed in a given time. Then, we ran our model considering data of 25 open-source communities on GitHub. Results of our study indicate that cultural and geographical dispersion impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle.
Ciasullo, Maria Vincenza; Montera, Raffaella; Mercuri, Francesco; Mugova, Shame
When Digitalization Meets Omnichannel in International Markets: A Case Study from the Agri-Food Industry Journal Article
In: Administrative Sciences, vol. 12, no. 2, pp. 68, 2022, ISSN: 2076-3387.
Abstract | Links | BibTeX | Tags: Digitalization, Management of Technology and Innovation, Strategy and Management
@article{ciasullo_when_2022,
title = {When Digitalization Meets Omnichannel in International Markets: A Case Study from the Agri-Food Industry},
author = {Maria Vincenza Ciasullo and Raffaella Montera and Francesco Mercuri and Shame Mugova},
url = {https://www.mdpi.com/2076-3387/12/2/68},
doi = {10.3390/admsci12020068},
issn = {2076-3387},
year = {2022},
date = {2022-06-01},
urldate = {2024-10-07},
journal = {Administrative Sciences},
volume = {12},
number = {2},
pages = {68},
abstract = {Digitalization is prompting small and medium-sized enterprises to structural and strategic transformations, also providing new opportunities to expand and succeed in foreign markets. However, relatively few studies have investigated emergent digital technologies in international business management. Contextually, there is still a dearth of research on the multi-faceted impacts of digitalization on omnichannel strategy characterizing most of the global business environment today. This paper, therefore, aims to examine the impact of digitalization on omnichannel choices adopted by internationalized SMEs. A qualitative approach, based on a single case study methodology, is adopted. An Italian agri-food SME is chosen as this industry is considered a key and distinctive pillar of Made in Italy in the international markets. Findings reveal the potential of digital technologies’ applications in an omnichannel environment, blurring the boundaries between channels, through a synergetic integration of them. This evidence contributes to the existing literature on technology management and omnichannel strategies in the international context by rereading these phenomena through a smart ecosystem lens. In addition, this study provides practical insights on how multiple channels adopted by Made in Italy SMEs can be integrated, managed, and operated synergistically on international markets to sustain a digitalized value creation.},
keywords = {Digitalization, Management of Technology and Innovation, Strategy and Management},
pubstate = {published},
tppubtype = {article}
}
Digitalization is prompting small and medium-sized enterprises to structural and strategic transformations, also providing new opportunities to expand and succeed in foreign markets. However, relatively few studies have investigated emergent digital technologies in international business management. Contextually, there is still a dearth of research on the multi-faceted impacts of digitalization on omnichannel strategy characterizing most of the global business environment today. This paper, therefore, aims to examine the impact of digitalization on omnichannel choices adopted by internationalized SMEs. A qualitative approach, based on a single case study methodology, is adopted. An Italian agri-food SME is chosen as this industry is considered a key and distinctive pillar of Made in Italy in the international markets. Findings reveal the potential of digital technologies’ applications in an omnichannel environment, blurring the boundaries between channels, through a synergetic integration of them. This evidence contributes to the existing literature on technology management and omnichannel strategies in the international context by rereading these phenomena through a smart ecosystem lens. In addition, this study provides practical insights on how multiple channels adopted by Made in Italy SMEs can be integrated, managed, and operated synergistically on international markets to sustain a digitalized value creation.
Pecorelli, Fabiano; Lujan, Savanna; Lenarduzzi, Valentina; Palomba, Fabio; Lucia, Andrea De
On the adequacy of static analysis warnings with respect to code smell prediction Journal Article
In: Empirical Software Engineering, vol. 27, no. 3, pp. 64, 2022, ISSN: 1382-3256, 1573-7616.
Abstract | Links | BibTeX | Tags: Code Smell Detection, Software, Software Engineering, Technical Debt Management
@article{pecorelliAdequacyStaticAnalysis2022,
title = {On the adequacy of static analysis warnings with respect to code smell prediction},
author = {Fabiano Pecorelli and Savanna Lujan and Valentina Lenarduzzi and Fabio Palomba and Andrea De Lucia},
url = {https://link.springer.com/10.1007/s10664-022-10126-5},
doi = {10.1007/s10664-022-10126-5},
issn = {1382-3256, 1573-7616},
year = {2022},
date = {2022-05-01},
urldate = {2024-07-07},
journal = {Empirical Software Engineering},
volume = {27},
number = {3},
pages = {64},
abstract = {Code smells are poor implementation choices that developers apply while evolving source code and that affect program maintainability. Multiple automated code smell detectors have been proposed: while most of them relied on heuristics applied over software metrics, a recent trend concerns the definition of machine learning techniques. However, machine learning-based code smell detectors still suffer from low accuracy: one of the causes is the lack of adequate features to feed machine learners. In this paper, we face this issue by investigating the role of static analysis warnings generated by three state-of-the-art tools to be used as features of machine learning models for the detection of seven code smell types. We conduct a three-step study in which we (1) verify the relation between static analysis warnings and code smells and the potential predictive power of these warnings; (2) build code smell prediction models exploiting and combining the most relevant features coming from the first analysis; (3) compare and combine the performance of the best code smell prediction model with the one achieved by a state of the art approach. The results reveal the low performance of the models exploiting static analysis warnings alone, while we observe significant improvements when combining the warnings with additional code metrics. Nonetheless, we still find that the best model does not perform better than a random model, hence leaving open the challenges related to the definition of ad-hoc features for code smell prediction.},
keywords = {Code Smell Detection, Software, Software Engineering, Technical Debt Management},
pubstate = {published},
tppubtype = {article}
}
Code smells are poor implementation choices that developers apply while evolving source code and that affect program maintainability. Multiple automated code smell detectors have been proposed: while most of them relied on heuristics applied over software metrics, a recent trend concerns the definition of machine learning techniques. However, machine learning-based code smell detectors still suffer from low accuracy: one of the causes is the lack of adequate features to feed machine learners. In this paper, we face this issue by investigating the role of static analysis warnings generated by three state-of-the-art tools to be used as features of machine learning models for the detection of seven code smell types. We conduct a three-step study in which we (1) verify the relation between static analysis warnings and code smells and the potential predictive power of these warnings; (2) build code smell prediction models exploiting and combining the most relevant features coming from the first analysis; (3) compare and combine the performance of the best code smell prediction model with the one achieved by a state of the art approach. The results reveal the low performance of the models exploiting static analysis warnings alone, while we observe significant improvements when combining the warnings with additional code metrics. Nonetheless, we still find that the best model does not perform better than a random model, hence leaving open the challenges related to the definition of ad-hoc features for code smell prediction.