Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
The rise of large language models for code has reshaped software development. Autonomous coding agents, able to create branches, open pull requests, and perform code reviews, now actively contribute to real-world projects. Their growing role offers a unique and timely opportunity to investigate AI-driven contributions and their effects on code quality, team dynamics, and software maintainability. In this work, we construct a novel dataset of approximately 110,000 open-source pull requests, including associated commits, comments, reviews, issues, and file changes, collectively representing millions of lines of source code. We compare five popular coding agents, including OpenAI Codex, Claude Code, GitHub Copilot, Google Jules, and Devin, examining how their usage differs in various development aspects such as merge frequency, edited file types, and developer interaction signals, including comments and reviews. Furthermore, we emphasize that code authoring and review are only a small part of the larger software engineering process, as the resulting code must also be maintained and updated over time. Hence, we offer several longitudinal estimates of survival and churn rates for agent-generated versus human-authored code. Ultimately, our findings indicate an increasing agent activity in open-source projects, although their contributions are associated with more churn over time compared to human-authored code.
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Razvan Mihai Popescu
claimed_verified
2026-04-03T09:08:23.273Z
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/avatars/493c16b52e1a9a940b4e02b6cd6c6cc3.svg
Razvan Popescu
Razvan27
user
Razvan27
69cea18de02fdd68b91b78dd
David Gros
69cea18de02fdd68b91b78de
Andrei Botocan
claimed_verified
2026-04-07T08:46:25.335Z
69d0749e5301f2941a7c2e5c
/avatars/52603c0294febb74fc26cd2407c0271b.svg
Andrei Botocan
Abotocan
user
Abotocan
69cea18de02fdd68b91b78df
Rahul Pandita
claimed_verified
2026-04-07T08:46:27.493Z
69d0091dd773af1c059cda24
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Rahul Pandita
rahulpandita
user
rahulpandita
69cea18de02fdd68b91b78e0
Prem Devanbu
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Maliheh Izadi
claimed_verified
2026-04-07T08:46:23.074Z
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Mali (Maliheh) Izadi
MalihehIzadi
user
MalihehIzadi
Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
The rise of large language models for code has reshaped software development. Autonomous coding agents, able to create branches, open pull requests, and perform code reviews, now actively contribute to real-world projects. Their growing role offers a unique and timely opportunity to investigate AI-driven contributions and their effects on code quality, team dynamics, and software maintainability. In this work, we construct a novel dataset of approximately 110,000 open-source pull requests, including associated commits, comments, reviews, issues, and file changes, collectively representing millions of lines of source code. We compare five popular coding agents, including OpenAI Codex, Claude Code, GitHub Copilot, Google Jules, and Devin, examining how their usage differs in various development aspects such as merge frequency, edited file types, and developer interaction signals, including comments and reviews. Furthermore, we emphasize that code authoring and review are only a small part of the larger software engineering process, as the resulting code must also be maintained and updated over time. Hence, we offer several longitudinal estimates of survival and churn rates for agent-generated versus human-authored code. Ultimately, our findings indicate an increasing agent activity in open-source projects, although their contributions are associated with more churn over time compared to human-authored code.
Analysis of AI coding agents' contributions to open-source projects reveals increased activity but higher code churn compared to human-authored code.
large language models autonomous coding agents pull requests code reviews software development open-source projects code quality team dynamics software maintainability survival rates churn rates