Examining Ownership Models in Software Teams: A Systematic Literature Review and a Replication Study

TL;DR

This paper systematically reviews software ownership models, develops a taxonomy, assesses study replication, and presents new findings on the relationship between contributor roles and code quality.

Contribution

It provides a comprehensive taxonomy of ownership artifacts, evaluates replication status, and offers new empirical insights into contributor impact on bugs in a real-world setting.

Findings

No strong correlation between minor contributors and bugs

No link between total developers and bugs

Significant correlation between major contributors and bugs

Abstract

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.