Predictability and Bottlenecks in the Development Flow of Open-Source Projects: an Analysis with Agile Metrics
Resumo
This study investigates predictability and potential bottlenecks in the software development flow of open-source projects. A quantitative analysis was conducted using flow metrics extracted from 3,233 public GitHub repositories. Cycle time, throughput, release interval, and the coefficient of variation were analyzed to assess flow predictability. The results revealed significant differences in metric variability across popularity levels and development stages. In addition, delivery capacity was associated with predictability indicators, particularly cycle time variability.
Palavras-chave:
Flow Metrics, Predictability, Bottlenecks, Agile Metrics
Referências
Hugo Borges, Andre Hora, and Marco Tulio Valente. 2018. What’s in a GitHub Star? Understanding Repository Starring Practices in Open Source Software. Journal of Systems and Software 146 (2018), 112–129. DOI: 10.1016/j.jss.2018.09.016
Hiero Henrique Barcelos Costa, Guilherme Marques de Oliveira, Victor Souza Salles, and Gleiph Ghiotto Lima Menezes. 2017. Tracking the Decisions to Select Repositories for Mining Software Repositories Experiments. In Proceedings of the 14th International Conference on Mining Software Repositories (MSR). IEEE, 360–371. DOI: 10.1109/MSR.2017.42
Wilton de Oliveira Bussab and Pedro Alberto Morettin. 2017. Estatística Básica (9 ed.). Saraiva, São Paulo.
Olive Jean Dunn. 1964. Multiple Comparisons Using Rank Sums. Technometrics 6, 3 (1964), 241–252.
John C. Flournoy, Carol S. Lee, Maggie Wu, and Catherine M. Hicks. 2025. No Silver Bullets: Why Understanding Software Cycle Time Is Messy, Not Magic. Empirical Software Engineering 30 (2025), 174. DOI: 10.1007/s10664-025-10735-w
Nicole Forsgren, Jez Humble, and Gene Kim. 2018. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations. IT Revolution Press.
Yiming Gao, Sushil K. Bajracharya, Joel Ossher, and Cristina V. Lopes. 2014. Boa: A Language and Infrastructure for Analyzing Ultra-Large-Scale Software Repositories. In Proceedings of the 2014 ACM/IEEE International Conference on Software Engineering (ICSE). ACM, 422–431. DOI: 10.1145/2568225.2568280
Sture Holm. 1979. A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics 6, 2 (1979), 65–70.
Jez Humble and David Farley. 2010. Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley Professional.
Douglas C. Montgomery and George C. Runger. 2010. Applied Statistics and Probability for Engineers (5 ed.). Wiley, New York.
Kai Petersen. 2010. An Empirical Study of Lead-Times in Incremental and Agile Software Development. In New Modeling Concepts for Today’s Software Processes, Jürgen Münch, Ye Yang, and Wilhelm Schäfer (Eds.). Lecture Notes in Computer Science, Vol. 6195. Springer, 345–356. DOI: 10.1007/978-3-642-14347-2_30
Kai Petersen and Claes Wohlin. 2011. Measuring the Flow in Lean Software Development. Software: Practice and Experience 41, 9 (2011), 975–996. DOI: 10.1002/spe.975
K. P. Pham and M. Neumann. 2024. How to Measure Performance in Agile Software Development? A Mixed-Method Study. In 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, Paris, France, 443–450. DOI: 10.1109/SEAA64295.2024.00074
J. Sadiq and Z. Rana. 2024. The Study of Characteristics of Self Organizing Teams in Agile. In 2024 21st International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 203–208. DOI: 10.1109/IBCAST61650.2024.10877114
David J. Sheskin. 2003. Handbook of Parametric and Nonparametric Statistical Procedures (3 ed.). Chapman and Hall/CRC, Boca Raton.
Oguzhan Tasci and Tugba Gurgen Erdogan. 2025. Comparative Analysis of AI Software and Traditional Software GitHub Repositories Using Process Mining. In Annals of Computer Science and Information Systems, Vol. 43. IEEE, 3421–3428. DOI: 10.15439/2025F3421
Mario F. Triola. 2017. Introdução à Estatística (12 ed.). LTC, Rio de Janeiro.
Daniel S. Vacanti. 2016. Actionable Agile Metrics for Predictability: An Introduction. ActionableAgile, Seattle, WA.
Akhila Sri Manasa Venigalla and Sridhar Chimalakonda. 2024. An Exploratory Study of Software Artifacts on GitHub from the Lens of Documentation. Information and Software Technology 169 (2024), 107425. DOI: 10.1016/j.infsof.2024.107425
Brennan Wilkes, Alessandra Maciel Paz Milani, and Margaret-Anne Storey. 2023. A Framework for Automating the Measurement of DevOps Research and Assessment (DORA) Metrics. In 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, Bogotá, Colombia, 62–72. DOI: 10.1109/ICSME58846.2023.00018
Claes Wohlin, Per Runeson, Martin Höst, Magnus C. Ohlsson, Björn Regnell, and Anders Wesslén. 2012. Experimentation in Software Engineering. Springer. DOI: 10.1007/978-3-642-29044-2
Hiero Henrique Barcelos Costa, Guilherme Marques de Oliveira, Victor Souza Salles, and Gleiph Ghiotto Lima Menezes. 2017. Tracking the Decisions to Select Repositories for Mining Software Repositories Experiments. In Proceedings of the 14th International Conference on Mining Software Repositories (MSR). IEEE, 360–371. DOI: 10.1109/MSR.2017.42
Wilton de Oliveira Bussab and Pedro Alberto Morettin. 2017. Estatística Básica (9 ed.). Saraiva, São Paulo.
Olive Jean Dunn. 1964. Multiple Comparisons Using Rank Sums. Technometrics 6, 3 (1964), 241–252.
John C. Flournoy, Carol S. Lee, Maggie Wu, and Catherine M. Hicks. 2025. No Silver Bullets: Why Understanding Software Cycle Time Is Messy, Not Magic. Empirical Software Engineering 30 (2025), 174. DOI: 10.1007/s10664-025-10735-w
Nicole Forsgren, Jez Humble, and Gene Kim. 2018. Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations. IT Revolution Press.
Yiming Gao, Sushil K. Bajracharya, Joel Ossher, and Cristina V. Lopes. 2014. Boa: A Language and Infrastructure for Analyzing Ultra-Large-Scale Software Repositories. In Proceedings of the 2014 ACM/IEEE International Conference on Software Engineering (ICSE). ACM, 422–431. DOI: 10.1145/2568225.2568280
Sture Holm. 1979. A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics 6, 2 (1979), 65–70.
Jez Humble and David Farley. 2010. Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley Professional.
Douglas C. Montgomery and George C. Runger. 2010. Applied Statistics and Probability for Engineers (5 ed.). Wiley, New York.
Kai Petersen. 2010. An Empirical Study of Lead-Times in Incremental and Agile Software Development. In New Modeling Concepts for Today’s Software Processes, Jürgen Münch, Ye Yang, and Wilhelm Schäfer (Eds.). Lecture Notes in Computer Science, Vol. 6195. Springer, 345–356. DOI: 10.1007/978-3-642-14347-2_30
Kai Petersen and Claes Wohlin. 2011. Measuring the Flow in Lean Software Development. Software: Practice and Experience 41, 9 (2011), 975–996. DOI: 10.1002/spe.975
K. P. Pham and M. Neumann. 2024. How to Measure Performance in Agile Software Development? A Mixed-Method Study. In 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, Paris, France, 443–450. DOI: 10.1109/SEAA64295.2024.00074
J. Sadiq and Z. Rana. 2024. The Study of Characteristics of Self Organizing Teams in Agile. In 2024 21st International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 203–208. DOI: 10.1109/IBCAST61650.2024.10877114
David J. Sheskin. 2003. Handbook of Parametric and Nonparametric Statistical Procedures (3 ed.). Chapman and Hall/CRC, Boca Raton.
Oguzhan Tasci and Tugba Gurgen Erdogan. 2025. Comparative Analysis of AI Software and Traditional Software GitHub Repositories Using Process Mining. In Annals of Computer Science and Information Systems, Vol. 43. IEEE, 3421–3428. DOI: 10.15439/2025F3421
Mario F. Triola. 2017. Introdução à Estatística (12 ed.). LTC, Rio de Janeiro.
Daniel S. Vacanti. 2016. Actionable Agile Metrics for Predictability: An Introduction. ActionableAgile, Seattle, WA.
Akhila Sri Manasa Venigalla and Sridhar Chimalakonda. 2024. An Exploratory Study of Software Artifacts on GitHub from the Lens of Documentation. Information and Software Technology 169 (2024), 107425. DOI: 10.1016/j.infsof.2024.107425
Brennan Wilkes, Alessandra Maciel Paz Milani, and Margaret-Anne Storey. 2023. A Framework for Automating the Measurement of DevOps Research and Assessment (DORA) Metrics. In 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, Bogotá, Colombia, 62–72. DOI: 10.1109/ICSME58846.2023.00018
Claes Wohlin, Per Runeson, Martin Höst, Magnus C. Ohlsson, Björn Regnell, and Anders Wesslén. 2012. Experimentation in Software Engineering. Springer. DOI: 10.1007/978-3-642-29044-2
Publicado
08/09/2026
Como Citar
SANTANA, Maria Eduarda Chrispim; ANDRADE, Michelle Hanne Soares de; TAVARES, Cleiton Silva.
Predictability and Bottlenecks in the Development Flow of Open-Source Projects: an Analysis with Agile Metrics. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA EM ENGENHARIA DE SOFTWARE - CONGRESSO BRASILEIRO DE SOFTWARE: TEORIA E PRÁTICA (CBSOFT), 17. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 56-61.
DOI: https://doi.org/10.5753/cbsoft_estendido.2026.31787.
