The Prevalence and Impact of Licenses in Open Software Projects

2026-06-22

AI Summary

This study addresses the unclear distribution of software licenses in open-source projects and their impact on code reuse and project activity. Leveraging a dataset of over 100 million open-source repositories, this work presents the first large-scale analysis of license adoption trends across programming language ecosystems and examines how license changes influence project activity over time. Through data mining and time-series comparisons, the authors find that most projects lack an explicit license, while permissive licenses have steadily increased in prevalence. Notably, the C ecosystem exhibits a preference for restrictive licenses, and transitioning from restrictive to permissive licensing correlates with decreased activity in C projects—contrasting sharply with Python projects, which show significantly enhanced activity following such transitions. These findings demonstrate that the effects of licensing strategies are highly dependent on the language ecosystem, challenging assumptions of universal applicability.

Abstract

The terms of how publicly available source code can be used are dictated by its license. The license (or its absence), in turn, affects what code the project may reuse and how its code can be (re)used and may also affect external participation and overall activity of the project. We aim to better understand the general state of license distribution overall and within language ecosystems and to investigate if license changes are associated with a noticeable variations of project output. To accomplish that we identify licenses and license types for over 100M software projects and find that most do not contain any license, that permissive licenses represent the bulk of most licenses, and that permissive licensing is representing an increasing proportion of all licenses over time. Restrictive licenses are more likely to be retained, however. There is a great variation among language ecosystems with C-language strongly favoring restrictive licenses. The analysis of license change impact comparing activity within one year of the adoption of the initial and final licenses shows that the change from restrictive to permissive license varies with the ecosystem. C-language ecosystems show reduced activity while Python shows increased activity when comparing restrictive to permissive license transition. Our results demonstrate dramatic changes in license type prevalence over time and find that the effects of license changes may have opposite effects depending on the language ecosystem.

Problem

• open source licenses
• license prevalence
• license change impact
• software ecosystems
• project activity

Innovation

• license prevalence
• open source licensing
• license change impact
• language ecosystems
• software project activity