Random Generation of Git Graphs
<|reference_start|>Random Generation of Git Graphs: Version Control Systems, such as Git and Mercurial, manage the history of a project as a Directed Acyclic Graph encoding the various divergences and synchronizations happening in its life cycle. A popular workflow in the industry, called the feature branch workflow, constrains these graphs to be of a particular shape: a unique main branch, and non-interfering feature branches. Here we focus on the uniform random generation of those graphs with n vertices, including k on the main branch, for which we provide three algorithms, for three different use-cases. The first, based on rejection, is efficient when aiming for small values of k (more precisely whenever k = O($\sqrt$ n)). The second takes as input any number k of commits in the main branch, but requires costly precalculation. The last one is a Boltzmann generator and enables us to generate very large graphs while targeting a constant k/n ratio. All these algorithms are linear in the size of their outputs.<|reference_end|>
@article{courtiel2024random,
    title={Random Generation of Git Graphs},
    author={Julien Courtiel (GREYC), Martin P'epin (GREYC)},
    journal={GASCom 2024, Jun 2024, Talence, France, France},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01902},
    primaryClass={cs.DS math.CO}
}