Branching Echoes: Exploring Branching Models as Sociotechnical Lenses for Community Smells
Resumo
Branching models coordinate how software changes are isolated, integrated, validated, and promoted. In machine learning (ML)-based systems, they also mediate coordination across source code, data, models, experiments, and heterogeneous roles. Yet branching is typically analyzed as a technical configuration-management mechanism, leaving unclear whether its repository traces can reveal sociotechnical dysfunction. We report an exploratory single-case study of an early-stage ML project developing a system to extract structured information from judicial health-litigation documents. We analyzed five repositories and survey responses from all three members of the AI team, triangulating the branching model prescribed to the team, practitioners’ perceptions, and enacted repository practices. Thematic analysis and repository mining revealed recurrent misalignments in issue traceability, pull-request discussion, experiment evaluation, and integration practices. These misalignments provided indicators consistent with the community smells Black Cloud, Sharing Villainy, and Cookbook Development; LoneWolf emerged only from perception data and was not corroborated by repository evidence.We do not claim that branching practices cause or automatically detect community smells. Instead, our findings suggest that branching-model evidence can provide a low-cost sociotechnical proxy for locating coordination risks that merit further investigation. We derive implications for MLOps governance and identify conditions that future multi-team studies should evaluate.
Palavras-chave:
Software Configuration Management, Branching Model, Machine Learning, Community Smells
Referências
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Arthur Bueno, Bruno B. P. Cafeo, Maria Istela Cagnin, and Awdren Fontão. 2025. Socio-Technical Smell Dynamics in Code Samples: A Multivocal Review of Their Emergence, Evolution, and Co-Occurrence. In Anais do XXXIX Simpósio Brasileiro de Engenharia de Software (SBES). DOI: 10.5753/sbes.2025.9647
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Nadia Nahar, Shurui Zhou, Grace Lewis, and Christian Kästner. 2022. Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process. In Proceedings of the 44th International Conference on Software Engineering (ICSE). 413–425. DOI: 10.1145/3510003.3510209
Nicholas Nelson, Caius Brindescu, Shane McKee, Anita Sarma, and Danny Dig. 2019. The life-cycle of merge conflicts: processes, barriers, and strategies. Empirical Software Engineering 24, 5 (2019), 2863–2906.
Martin P Robillard, Deeksha M Arya, Neil A Ernst, Jin LC Guo, Maxime Lamothe, Mathieu Nassif, Nicole Novielli, Alexander Serebrenik, Igor Steinmacher, and Klaas-Jan Stol. 2024. Communicating study design trade-offs in software engineering. ACM Transactions on Software Engineering and Methodology 33, 5 (2024), 1–10.
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Alex Serban, Koen van der Blom, Holger Hoos, and Joost Visser. 2020. Adoption and Effects of Software Engineering Best Practices in Machine Learning. In Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). ACM, Article 3, 12 pages. DOI: 10.1145/3382494.3410681
Emad Shihab, Christian Bird, and Thomas Zimmermann. 2012. The Effect of Branching Strategies on Software Quality. In Proceedings of the 2012 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM ’12). ACM, 301–310. DOI: 10.1145/2372251.2372305
Davide Spadini, Maurício Aniche, and Alberto Bacchelli. 2018. PyDriller: Python framework for mining software repositories. In Proceedings of the 2018 26th ACM Joint meeting on european software engineering conference and symposium on the foundations of software engineering. 908–911.
Mark Swillus, Rashina Hoda, and Andy Zaidman. 2026. On the Emergence of Testing Strategies: A Socio-Technical Grounded Theory. Empirical Software Engineering 31, 5, Article 147 (2026). DOI: 10.1007/s10664-026-10828-0
Damian A. Tamburri, Philippe Kruchten, Patricia Lago, and Hans van Vliet. 2015. Social debt in software engineering: insights from industry. Journal of Internet Services and Applications 6, 1, Article 10 (2015). DOI: 10.1186/s13174-015-0024-6
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. DOI: 10.1109/TSE.2019.2901490
Weiqin Zou, Weiqiang Zhang, Xin Xia, Reid Holmes, and Zhenyu Chen. 2019. Branch Use in Practice: A Large-Scale Empirical Study of 2,923 Projects on GitHub. In 2019 19th IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE, 306–317. DOI: 10.1109/QRS.2019.00047
Uğur Can Altun, Ismail Sergen Göçmen, Emre Sülün, Erdem Tuna, and Eray Tüzün. 2025. Process smells in practice: an evaluative case study. Empirical Software Engineering 30, 5 (2025), 115. DOI: 10.1007/s10664-025-10664-8
Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. 2019. Software Engineering for Machine Learning: A Case Study. In Proceedings of the 41st IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 291–300. DOI: 10.1109/ICSE-SEIP.2019.00042
Giusy Annunziata, Stefano Lambiase, Damian A. Tamburri, Willem-Jan van den Heuvel, Fabio Palomba, Gemma Catolino, Filomena Ferrucci, and Andrea De Lucia. 2025. Uncovering Community Smells in Machine Learning-Enabled Systems: Causes, Effects, and Mitigation Strategies. ACM Transactions on Software Engineering and Methodology 34, 6, Article 176 (2025). DOI: 10.1145/3712198
Anders Arpteg, Björn Brinne, Luka Crnkovic-Friis, and Jan Bosch. 2018. Software Engineering Challenges of Deep Learning. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 50–59. DOI: 10.1109/SEAA.2018.00018
Christian Bird and Thomas Zimmermann. 2012. Assessing the Value of Branches with What-if Analysis. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE 2012). ACM, Article 45, 11 pages. DOI: 10.1145/2393596.2393648
Christian Bird, Thomas Zimmermann, and Alex Teterev. 2011. A theory of branches as goals and virtual teams. In Proceedings of the 4th International Workshop on Cooperative and Human Aspects of Software Engineering. 53–56.
Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101. DOI: 10.1191/1478088706qp063oa
Arthur Bueno, Bruno B. P. Cafeo, Maria Istela Cagnin, and Awdren Fontão. 2025. Socio-Technical Smell Dynamics in Code Samples: A Multivocal Review of Their Emergence, Evolution, and Co-Occurrence. In Anais do XXXIX Simpósio Brasileiro de Engenharia de Software (SBES). DOI: 10.5753/sbes.2025.9647
Eduardo Caballero-Espinosa, Jeffrey C. Carver, and Kimberly Stowers. 2023. Community smells—The sources of social debt: A systematic literature review. Information and Software Technology 153 (2023), 107078. DOI: 10.1016/j.infsof.2022.107078
Marcelo Cataldo, James D. Herbsleb, and Kathleen M. Carley. 2008. Sociotechnical congruence: a framework for assessing the impact of technical and work dependencies on software development productivity. In Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). 2–11. DOI: 10.1145/1414004.1414008
Shamse Tasnim Cynthia, Nuri Almarimi, and Banani Roy. 2025. How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?. In Proceedings of the 19th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). DOI: 10.1109/ESEM64174.2025.00072
Beyza Eken, Samodha Pallewatta, Nguyen Khoi Tran, Ayse Tosun, and Muhammad Ali Babar. 2026. A Multivocal Review of MLOps Practices, Challenges and Open Issues. Comput. Surveys 58, 2, Article 39 (2026), 39:1–39:35 pages. DOI: 10.1145/3747346
Pedro Lopes, Paola Accioly, Paulo Borba, and Vitor Menezes. 2025. Choosing the Right Git Workflow: A Comparative Analysis of Trunk-based vs. Branch-based Approaches. In Anais do XXXIX Simpósio Brasileiro de Engenharia de Software (SBES). DOI: 10.5753/sbes.2025.9903
Alina Mailach and Norbert Siegmund. 2023. Socio-Technical Anti-Patterns in BuildingML-Enabled Software: Insights from Leaders on the Forefront. In Proceedings of the 45th International Conference on Software Engineering (ICSE). 690–702. DOI: 10.1109/ICSE48619.2023.00067
Nadia Nahar, Haoran Zhang, Grace Lewis, Shurui Zhou, and Christian Kästner. 2025. The Product Beyond the Model: An Empirical Study of Repositories of Open-Source ML Products. In Proceedings of the 47th International Conference on Software Engineering (ICSE). DOI: 10.1109/ICSE55347.2025.00006
Nadia Nahar, Shurui Zhou, Grace Lewis, and Christian Kästner. 2022. Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process. In Proceedings of the 44th International Conference on Software Engineering (ICSE). 413–425. DOI: 10.1145/3510003.3510209
Nicholas Nelson, Caius Brindescu, Shane McKee, Anita Sarma, and Danny Dig. 2019. The life-cycle of merge conflicts: processes, barriers, and strategies. Empirical Software Engineering 24, 5 (2019), 2863–2906.
Martin P Robillard, Deeksha M Arya, Neil A Ernst, Jin LC Guo, Maxime Lamothe, Mathieu Nassif, Nicole Novielli, Alexander Serebrenik, Igor Steinmacher, and Klaas-Jan Stol. 2024. Communicating study design trade-offs in software engineering. ACM Transactions on Software Engineering and Methodology 33, 5 (2024), 1–10.
Per Runeson and Martin Höst. 2009. Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering 14, 2 (2009), 131–164. DOI: 10.1007/s10664-008-9102-8
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison. 2015. Hidden Technical Debt in Machine Learning Systems. In Advances in Neural Information Processing Systems 28 (NIPS 2015). Curran Associates, Inc., 2503–2511. [link]
Alex Serban, Koen van der Blom, Holger Hoos, and Joost Visser. 2020. Adoption and Effects of Software Engineering Best Practices in Machine Learning. In Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). ACM, Article 3, 12 pages. DOI: 10.1145/3382494.3410681
Emad Shihab, Christian Bird, and Thomas Zimmermann. 2012. The Effect of Branching Strategies on Software Quality. In Proceedings of the 2012 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM ’12). ACM, 301–310. DOI: 10.1145/2372251.2372305
Davide Spadini, Maurício Aniche, and Alberto Bacchelli. 2018. PyDriller: Python framework for mining software repositories. In Proceedings of the 2018 26th ACM Joint meeting on european software engineering conference and symposium on the foundations of software engineering. 908–911.
Mark Swillus, Rashina Hoda, and Andy Zaidman. 2026. On the Emergence of Testing Strategies: A Socio-Technical Grounded Theory. Empirical Software Engineering 31, 5, Article 147 (2026). DOI: 10.1007/s10664-026-10828-0
Damian A. Tamburri, Philippe Kruchten, Patricia Lago, and Hans van Vliet. 2015. Social debt in software engineering: insights from industry. Journal of Internet Services and Applications 6, 1, Article 10 (2015). DOI: 10.1186/s13174-015-0024-6
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. DOI: 10.1109/TSE.2019.2901490
Weiqin Zou, Weiqiang Zhang, Xin Xia, Reid Holmes, and Zhenyu Chen. 2019. Branch Use in Practice: A Large-Scale Empirical Study of 2,923 Projects on GitHub. In 2019 19th IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE, 306–317. DOI: 10.1109/QRS.2019.00047
Publicado
11/09/2026
Como Citar
MENEZES, Diogo de Lima; XAVIER, Eos; JYOBOJI, Rafael Tsutomu; BORGES, Hudson Silva; FONTÃO, Awdren de Lima.
Branching Echoes: Exploring Branching Models as Sociotechnical Lenses for Community Smells. In: WORKSHOP DE VISUALIZAÇÃO, EVOLUÇÃO E MANUTENÇÃO DE SOFTWARE (VEM), 14. , 2026, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 137-143.
DOI: https://doi.org/10.5753/vem.2026.32994.
