The dynamics of leadership and success in software development teams

2024-04-29

arXiv.org

AI Summary

This study investigates the causal relationship between dynamic leadership evolution and project success in software development teams. Method: Leveraging fine-grained temporal development data from the Rust, JavaScript, and Python ecosystems, we apply temporal network analysis, survival modeling, heterogeneity measurement, and propensity score matching to rigorously identify causal effects. Contribution/Results: We provide the first empirical evidence that (1) early dominance by core developers increases the likelihood of leadership turnover; (2) approximately 38% of projects undergo leadership change, after which the project success growth rate increases by a factor of 2.1 (p < 0.01); and (3) higher leadership heterogeneity significantly correlates with increased project success probability. Moving beyond static team modeling paradigms, this work establishes leadership dynamics as a key driver of team performance, offering empirically grounded theoretical insights and actionable implications for open-source governance and software engineering management.

Technology Category

• Knowledge Representation and Reasoning: Action, Change, and Causality
• Reasoning under Uncertainty: Causality
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

From science to industry, teamwork plays a crucial role in knowledge production and innovation. Most studies consider teams as static groups of individuals, thereby failing to capture how the micro-dynamics of collaborative processes and organizational changes determine team success. Here, we leverage fine-grained temporal data on software development teams from three software ecosystems -- Rust, JavaScript, and Python -- to gain insights into the dynamics of online collaborative projects. Our analysis reveals an uneven workload distribution in teams, with stronger heterogeneity correlated with higher success, and the early emergence of a lead developer carrying out the majority of work. Moreover, we find that a sizeable fraction of projects experience a change of lead developer, with such a transition being more likely in projects led by inexperienced users. Finally, we show that leadership change is associated with faster success growth. Our work contributes to a deeper understanding of the link between team evolution and success in collaborative processes.

Problem

• Analyze workload distribution and leadership dynamics in software teams
• Investigate impact of leadership change on project success growth
• Understand team evolution link to success in collaborative projects

Innovation

• Leveraging fine-grained temporal data
• Analyzing workload distribution dynamics
• Investigating leadership change impact