Evolutionary Analysis of the Co-occurrence between Code Smells and Community Smells in Code Samples
Resumo
Context: Code samples are widely used to demonstrate best practices and facilitate framework adoption, promoting productivity and innovation. In open-source projects, their evolution often involves diverse contributions, introducing technical complexity and challenges in community collaboration. This scenario can leads to the co-occurrence of Code Smells and Community Smells, impacting software quality, collaboration, and the sustainability of developer communities. Problem: The simultaneous evolution of Code Smells and Community Smells degrades code quality, reduces developer cohesion, and lowers collaborative efficiency, increasing the likelihood of project failure and making projects less appealing to new contributors. Solution: This study investigates how the co-occurrence and evolution of Code Smells and Community Smells interact over time. It proposes strategies based on metrics and temporal analysis to mitigate these impacts, improve maintainability, and strengthen community dynamics. Information Systems Theory: Social Network Theory provides a foundation to analyze the influence of human interactions on project evolution and team cohesion. Method: We mined open-source repositories—using techniques such as commit history mining, bug log analysis, and code metric extraction—and temporal analysis to quantitatively track and correlate the evolution of Code Smells and Community Smells across project milestones. Results Summary: Local commit surges trigger transient complexity spikes; geographic dispersion reduces cohesion; and diverse teams correlate with controlled code growth. Overall, smells interact to degrade maintainability and collaboration. Contributions: Addressing the SBSI Grand Challenges (2016–2026), this study integrates technical and social perspectives, fostering sustainable practices in open-source projects and enhancing the resilience and effectiveness of developer communities.
Palavras-chave:
Code Smells, Community Smells, Code Samples, Software Quality, Software Evolution, Developer Communities
Referências
2018. How do community smells influence code smells? Association for Computing Machinery, New York, NY, USA.
Ashraf Abdou and Nagy Darwish. 2022. Severity classification of software code smells using machine learning techniques: A comparative study. Journal of Software: Evolution and Process 36, 1 (2022), e2454.
Nuri Almarim, Ali Ouni, and Mohamed Wiem Mkaouer. 2020. Learning to Detect Community Smells in Open Source Software Projects.
Nuri Almarimi, Ali Ouni, Moataz Chouchen, and Mohamed Wiem Mkaouer. 2023. csDetector: An Open Source Tool for Community Smells Detection. ETS Montreal, University of Quebec.
Abdullah Almogahed, Hairulnizam Mahdin, Mazidah Mat Rejab, Abdulwadood Alawadhi, Samera Obaid Barraood, Manal Othman, Omar Al-Jamili, Abdulwahab Ali Almazroi, and Shazlyn Milleana Shaharudin. 2024. Code Refactoring for Software Reusability: An Experimental Study. In 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA). 1–6.
Giusy Annunziata, Carmine Ferrara, Stefano Lambiase, Fabio Palomba, Gemma Catolino, Filomena Ferrucci, and Andrea De Lucia. 2024. An Empirical Study on the Relation Between Programming Languages and the Emergence of Community Smells. In 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE Computer Society, Los Alamitos, CA, USA, 268–275.
Gemma Catolino, Fabio Palomba, Damian A. Tamburri, and Alexander Serebrenik. 2021. Understanding Community Smells Variability: A Statistical Approach. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS). 77–86.
Zhifei Chen, Wanwangying Ma, Lin Chen, and Wei Song. 2022. Collaboration in software ecosystems: A study of work groups in open environment. Information and Software Technology 145 (2022), 106849.
Matheus Albuquerque de Melo. 2023. A Multi-Faceted Analysis of How Organizations Create and Maintain Code Samples. Dissertação de Mestrado. Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil.
Serge Demeyer and Tom Mens. 2008. Software Evolution. Springer, Boston.
Rodrigo dos Santos, George Valença, Davi Viana, Bernardo Estácio, Awdren Fontão, Sabrina Marczak, Claudia Werner, Carina Alves, Tayana Conte, and Rafael Prikladnicki. 2014. Qualidade em Ecossistemas de Software: Desafios e Oportunidades de Pesquisa.
Martin Fowler and Kent Beck. 2018. Refactoring: Improving the Design of Existing Code. Vol. 1. Addison-Wesley Professional, Boston, 464. The guide to how to transform code with a safe and rapid process, vital to keeping it cheap and easy to modify for future needs.
Oscar Franco-Bedoya, David Ameller, Dolors Costal, and Xavier Franch. 2017. Open Source Software Ecosystems: A Systematic Mapping. Information and Software Technology (2017).
Hadi Hemmati, Sarah Nadi, Olga Baysal, Oleksii Kononenko, Wei Wang, Reid Holmes, and Michael W. Godfrey. 2013. The msr cookbook: Mining a decade of research. In 2013 10th Working Conference on Mining Software Repositories (MSR). IEEE, 343–352.
Zi-Jie Huang, Zhi-Qing Shao, Gui-Sheng Fan, Hui-Qun Yu, Xing-Guang Yang, and Kang Yang. 2022. Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics. Journal of Computer Science and Technology 37, 1 (2022), 182–206.
N.A.A. Khleel and K. Nehéz. 2024. Improving accuracy of code smells detection using machine learning with data balancing techniques. Journal of Supercomputing 80 (2024), 21048–21093.
Noah Lambaria and Tom Černý. 2022. A Data Analysis Study of Code Smells within Java Repositories. In Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, Vol. 32. ACSIS, 313–318.
Riasat Mahbub, Mohammad Masudur Rahman, and Muhammad Ahsanul Habib. 2024. On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software. In 2024 IEEE International Conference on Source Code Analysis and Manipulation (SCAM). 154–165.
Riasat Mahbub, Mohammad Masudur Rahman, and Muhammad Ahsanul Habib. 2024. On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software.
Júlio Martins, Carla Bezerra, Anderson Uchôa, and Alessandro Garcia. 2020. Are Code Smell Co-occurrences Harmful to Internal Quality Attributes? A Mixed-Method Study. In Proceedings of the 34th Brazilian Symposium on Software Engineering. Association for Computing Machinery, New York, NY, USA, 52–61.
Gabriel Menezes, Willian Braga, Awdren Fontão, Andre Hora, and Bruno Cafeo. 2022. Assessing the Impact of Code Samples Evolution on Developers’ Questions. In 36th Brazilian Symposium on Software Engineering (SBES 2022). 1–10.
Gabriel Menezes, Bruno Cafeo, and Andre Hora. 2019. Framework Code Samples: How Are They Maintained and Used by Developers?. In IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). 564–574.
Behnaz Moradi-Jamei, Brandon L. Kramer, J. Bayoán Santiago Calderón, and Gizem Korkmaz. 2021. Community Formation and Detection on GitHub Collaboration Networks. In 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 244–252.
Tomasz Neugebauer, Pierre Lasou, and Pamela Carson. 2024. What are the characteristic community smells influencing the sustainability of open-source repository software communities? Open Repositories 2024 (OR2024).
Fabio Palomba, Annibale Panichella, Andrea De Lucia, Rocco Oliveto, and Andy Zaidman. 2016. A textual-based technique for Smell Detection. In 2016 IEEE 24th International Conference on Program Comprehension (ICPC). 1–10. DOI: 10.1109/ICPC.2016.7503704
Antonio Della Porta, Stefano Lambiase, Gemma Catolino, Filomena Ferrucci, and Fabio Palomba. 2025. A Novel, Tool-Supported Catalog of Community Smell Symptoms. In Proceedings of the Brazilian Symposium on Information Systems (SBSI25). Recife, PE, Brazil.
Bárbara Beato Ribeiro, Luiz Alexandre Costa, Juliana Carvalho Silva Do Out ao, and Rodrigo Pereira Dos Santos. 2023. Towards Power Relationship Dynamics and Community Smells in the Proprietary Software Ecosystem. Association for Computing Machinery, New York, NY, USA, 142–147.
Tushar Sharma. 2024. Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0. In 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR). 284–288.
Thiciane Suely Couto Silva, Fabio Gomes Rocha, and Rodrigo Pereira dos Santos. 2019. Resource Demand Management in Java Ecosystem. In Proceedings of the XV Brazilian Symposium on Information Systems. Association for Computing Machinery, New York, NY, USA, Article 3.
Margaret-Anne Storey, Leif Singer, Brendan Cleary, Fernando Figueira Filho, and Alexey Zagalsky. 2014. The (R) Evolution of social media in software engineering. In Future of Software Engineering Proceedings. Association for Computing Machinery, New York, NY, USA, 100–116.
D. A. Tamburri, P. Lago, and H. V. Vliet. 2013.organizational social structures for software engineering. ACM Comput. Surv. 46, 1 (jul 2013), 3:1–3:35.
Damian A. Tamburri, Fabio Palomba, and Rick Kazman. 2021. Exploring Community Smells in Open-Source: An Automated Approach. IEEE Transactions on Software Engineering 47, 3 (2021), 630–652.
Stuti Tandon, Vijay Kumar, and V. B. Singh. 2024. Study of Code Smells: A Review and Research Agenda. International Journal of Mathematical, Engineering & Management Sciences 9, 3 (2024), 472.
Liang Wang, Ying Li, Jierui Zhang, and Xianping Tao. 2022. Quantitative Analysis of Community Evolution in Developer Social Networks Around Open Source Software Projects. arXiv:2205.09935 [cs.SE]
Zeyi Wang, Eric Bridgeford, ShangsiWang, Joshua T. Vogelstein, and Brian Caffo. 2024. Statistical Analysis of Data Repeatability Measures. (2024). arXiv:2005.11911
Quanxin Yang, Dongjin Yu, Xin Chen, Yihang Xu, Wangliang Yan, and Bin Hu. 2024. Feature envy detection based on cross-graph local semantics matching. Information and Software Technology 174 (2024), 107515.
Ashraf Abdou and Nagy Darwish. 2022. Severity classification of software code smells using machine learning techniques: A comparative study. Journal of Software: Evolution and Process 36, 1 (2022), e2454.
Nuri Almarim, Ali Ouni, and Mohamed Wiem Mkaouer. 2020. Learning to Detect Community Smells in Open Source Software Projects.
Nuri Almarimi, Ali Ouni, Moataz Chouchen, and Mohamed Wiem Mkaouer. 2023. csDetector: An Open Source Tool for Community Smells Detection. ETS Montreal, University of Quebec.
Abdullah Almogahed, Hairulnizam Mahdin, Mazidah Mat Rejab, Abdulwadood Alawadhi, Samera Obaid Barraood, Manal Othman, Omar Al-Jamili, Abdulwahab Ali Almazroi, and Shazlyn Milleana Shaharudin. 2024. Code Refactoring for Software Reusability: An Experimental Study. In 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA). 1–6.
Giusy Annunziata, Carmine Ferrara, Stefano Lambiase, Fabio Palomba, Gemma Catolino, Filomena Ferrucci, and Andrea De Lucia. 2024. An Empirical Study on the Relation Between Programming Languages and the Emergence of Community Smells. In 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE Computer Society, Los Alamitos, CA, USA, 268–275.
Gemma Catolino, Fabio Palomba, Damian A. Tamburri, and Alexander Serebrenik. 2021. Understanding Community Smells Variability: A Statistical Approach. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS). 77–86.
Zhifei Chen, Wanwangying Ma, Lin Chen, and Wei Song. 2022. Collaboration in software ecosystems: A study of work groups in open environment. Information and Software Technology 145 (2022), 106849.
Matheus Albuquerque de Melo. 2023. A Multi-Faceted Analysis of How Organizations Create and Maintain Code Samples. Dissertação de Mestrado. Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil.
Serge Demeyer and Tom Mens. 2008. Software Evolution. Springer, Boston.
Rodrigo dos Santos, George Valença, Davi Viana, Bernardo Estácio, Awdren Fontão, Sabrina Marczak, Claudia Werner, Carina Alves, Tayana Conte, and Rafael Prikladnicki. 2014. Qualidade em Ecossistemas de Software: Desafios e Oportunidades de Pesquisa.
Martin Fowler and Kent Beck. 2018. Refactoring: Improving the Design of Existing Code. Vol. 1. Addison-Wesley Professional, Boston, 464. The guide to how to transform code with a safe and rapid process, vital to keeping it cheap and easy to modify for future needs.
Oscar Franco-Bedoya, David Ameller, Dolors Costal, and Xavier Franch. 2017. Open Source Software Ecosystems: A Systematic Mapping. Information and Software Technology (2017).
Hadi Hemmati, Sarah Nadi, Olga Baysal, Oleksii Kononenko, Wei Wang, Reid Holmes, and Michael W. Godfrey. 2013. The msr cookbook: Mining a decade of research. In 2013 10th Working Conference on Mining Software Repositories (MSR). IEEE, 343–352.
Zi-Jie Huang, Zhi-Qing Shao, Gui-Sheng Fan, Hui-Qun Yu, Xing-Guang Yang, and Kang Yang. 2022. Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics. Journal of Computer Science and Technology 37, 1 (2022), 182–206.
N.A.A. Khleel and K. Nehéz. 2024. Improving accuracy of code smells detection using machine learning with data balancing techniques. Journal of Supercomputing 80 (2024), 21048–21093.
Noah Lambaria and Tom Černý. 2022. A Data Analysis Study of Code Smells within Java Repositories. In Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, Vol. 32. ACSIS, 313–318.
Riasat Mahbub, Mohammad Masudur Rahman, and Muhammad Ahsanul Habib. 2024. On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software. In 2024 IEEE International Conference on Source Code Analysis and Manipulation (SCAM). 154–165.
Riasat Mahbub, Mohammad Masudur Rahman, and Muhammad Ahsanul Habib. 2024. On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software.
Júlio Martins, Carla Bezerra, Anderson Uchôa, and Alessandro Garcia. 2020. Are Code Smell Co-occurrences Harmful to Internal Quality Attributes? A Mixed-Method Study. In Proceedings of the 34th Brazilian Symposium on Software Engineering. Association for Computing Machinery, New York, NY, USA, 52–61.
Gabriel Menezes, Willian Braga, Awdren Fontão, Andre Hora, and Bruno Cafeo. 2022. Assessing the Impact of Code Samples Evolution on Developers’ Questions. In 36th Brazilian Symposium on Software Engineering (SBES 2022). 1–10.
Gabriel Menezes, Bruno Cafeo, and Andre Hora. 2019. Framework Code Samples: How Are They Maintained and Used by Developers?. In IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). 564–574.
Behnaz Moradi-Jamei, Brandon L. Kramer, J. Bayoán Santiago Calderón, and Gizem Korkmaz. 2021. Community Formation and Detection on GitHub Collaboration Networks. In 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 244–252.
Tomasz Neugebauer, Pierre Lasou, and Pamela Carson. 2024. What are the characteristic community smells influencing the sustainability of open-source repository software communities? Open Repositories 2024 (OR2024).
Fabio Palomba, Annibale Panichella, Andrea De Lucia, Rocco Oliveto, and Andy Zaidman. 2016. A textual-based technique for Smell Detection. In 2016 IEEE 24th International Conference on Program Comprehension (ICPC). 1–10. DOI: 10.1109/ICPC.2016.7503704
Antonio Della Porta, Stefano Lambiase, Gemma Catolino, Filomena Ferrucci, and Fabio Palomba. 2025. A Novel, Tool-Supported Catalog of Community Smell Symptoms. In Proceedings of the Brazilian Symposium on Information Systems (SBSI25). Recife, PE, Brazil.
Bárbara Beato Ribeiro, Luiz Alexandre Costa, Juliana Carvalho Silva Do Out ao, and Rodrigo Pereira Dos Santos. 2023. Towards Power Relationship Dynamics and Community Smells in the Proprietary Software Ecosystem. Association for Computing Machinery, New York, NY, USA, 142–147.
Tushar Sharma. 2024. Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0. In 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR). 284–288.
Thiciane Suely Couto Silva, Fabio Gomes Rocha, and Rodrigo Pereira dos Santos. 2019. Resource Demand Management in Java Ecosystem. In Proceedings of the XV Brazilian Symposium on Information Systems. Association for Computing Machinery, New York, NY, USA, Article 3.
Margaret-Anne Storey, Leif Singer, Brendan Cleary, Fernando Figueira Filho, and Alexey Zagalsky. 2014. The (R) Evolution of social media in software engineering. In Future of Software Engineering Proceedings. Association for Computing Machinery, New York, NY, USA, 100–116.
D. A. Tamburri, P. Lago, and H. V. Vliet. 2013.organizational social structures for software engineering. ACM Comput. Surv. 46, 1 (jul 2013), 3:1–3:35.
Damian A. Tamburri, Fabio Palomba, and Rick Kazman. 2021. Exploring Community Smells in Open-Source: An Automated Approach. IEEE Transactions on Software Engineering 47, 3 (2021), 630–652.
Stuti Tandon, Vijay Kumar, and V. B. Singh. 2024. Study of Code Smells: A Review and Research Agenda. International Journal of Mathematical, Engineering & Management Sciences 9, 3 (2024), 472.
Liang Wang, Ying Li, Jierui Zhang, and Xianping Tao. 2022. Quantitative Analysis of Community Evolution in Developer Social Networks Around Open Source Software Projects. arXiv:2205.09935 [cs.SE]
Zeyi Wang, Eric Bridgeford, ShangsiWang, Joshua T. Vogelstein, and Brian Caffo. 2024. Statistical Analysis of Data Repeatability Measures. (2024). arXiv:2005.11911
Quanxin Yang, Dongjin Yu, Xin Chen, Yihang Xu, Wangliang Yan, and Bin Hu. 2024. Feature envy detection based on cross-graph local semantics matching. Information and Software Technology 174 (2024), 107515.
Publicado
19/05/2025
Como Citar
BUENO, Arthur; BEZERRA, Carla; BORGES, Hudson S.; CAFEO, Bruno B. P.; CAGNIN, Maria Istela; FONTÃO, Awdren.
Evolutionary Analysis of the Co-occurrence between Code Smells and Community Smells in Code Samples. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 21. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 654-662.
DOI: https://doi.org/10.5753/sbsi.2025.246612.
