Choosing the Right Git Workflow: A Comparative Analysis of Trunk-based vs. Branch-based Approaches
July 11, 2025 Β· Declared Dead Β· π Brazilian Symposium on Software Engineering
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Pedro Lopes, Paola Accioly, Paulo Borba, Vitor Menezes
arXiv ID
2507.08943
Category
cs.SE: Software Engineering
Citations
1
Venue
Brazilian Symposium on Software Engineering
Last Checked
5 months ago
Abstract
Git has become one of the most widely used version control systems today. Among its distinguishing features, its ability to easily and quickly create branches stands out, allowing teams to customize their workflows. In this context, various formats of collaborative development workflows using Git have emerged and gained popularity among software engineers. We can categorize such workflows into two main types: branch-based workflows and trunk-based workflows. Branch-based workflows typically define a set of remote branches with well-defined objectives, such as feature branches, a branch for feature integration, and a main branch. The goal is to migrate changes from the most isolated branch to the main one shared by all as the code matures. In this category, GitFlow stands out as the most popular example. In contrast, trunk-based workflows have a single remote branch where developers integrate their changes directly. In this range of options, choosing a workflow that maximizes team productivity while promoting software quality becomes a non-trivial task. Despite discussions on forums, social networks, and blogs, few scientific articles have explored this topic. In this work, we provide evidence on how Brazilian developers work with Git workflows and what factors favor or hinder the use of each model. To this end, we conducted semi-structured interviews and a survey with software developers. Our results indicate that trunk-based development favors fast-paced projects with experienced and smaller teams, while branch-based development suits less experienced and larger teams better, despite posing management challenges.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted