Examining Ownership Models in Software Teams: A Systematic Literature Review and a Replication Study
<|reference_start|>Examining Ownership Models in Software Teams: A Systematic Literature Review and a Replication Study: Effective ownership of software artifacts, particularly code, is crucial for accountability, knowledge sharing, and code quality enhancement. Researchers have proposed models linking ownership of software artifacts with developer performance and code quality. Our study aims to systematically examine various ownership models and provide a structured literature overview. Conducting a systematic literature review, we identified 79 relevant papers published between 2005 and 2022. We developed a taxonomy of ownership artifacts based on type, owners, and degree of ownership, along with compiling modeling variables and analytics types used in each study. Additionally, we assessed the replication status of each study. As a result, we identified nine distinct software artifacts whose ownership has been discussed in the literature, with "Code" being the most frequently analyzed artifact. We found that only three papers (3.79%) provided code and data, whereas nine papers (11.4%) provided only data. Using our systematic literature review results, we replicated experiments on nine priority projects at \texttt{Brightsquid}. The company aimed to compare its code quality against ownership factors in other teams, so we conducted a replication study using their data. Unlike prior studies, we found no strong correlation between minor contributors and bug numbers. Surprisingly, we found no strong link between the total number of developers modifying a file and bug counts, contrasting previous findings. However, we observed a significant correlation between major contributors and bug counts, diverging from earlier research.<|reference_end|>
@article{koana2024examining,
    title={Examining Ownership Models in Software Teams: A Systematic Literature
  Review and a Replication Study},
    author={Umme Ayman Koana, Quang Hy Le, Shadikur Rahman, Chris Carlson, Francis
  Chew, Maleknaz Nayebi},
    journal={arXiv preprint arXiv:2405.15665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15665},
    primaryClass={cs.SE}
}