Measuring Software Innovation with Open Source Software Development Data

TL;DR

This paper proposes a new measure of software innovation based on open source development activity, analyzing dependency growth and release complexity across ecosystems to better understand innovation dynamics.

Contribution

Introduces a novel OSS-based innovation metric using GitHub data, accounting for ecosystem-specific release patterns and maturity-dependent adoption behaviors.

Findings

Semantic versioning types predict dependency growth differently across ecosystems.

Minor releases show consistent adoption, while major and patch releases vary.

Technical complexity has minimal impact on downstream adoption.

Abstract

Existing innovation metrics inadequately capture software innovation, creating blind spots for researchers and policymakers seeking to understand and foster technological innovation in an increasingly software-defined economy. This paper introduces a novel measure of software innovation based on open source software (OSS) development activity on GitHub. We examine the dependency growth and release complexity among 350,000 unique releases from 33,000 unique packages across the JavaScript, Python, and Ruby ecosystems over two years post-release. We find that the semantic versioning types of OSS releases exhibit ecosystem-specific and maturity-dependent patterns in predicting one-year dependency growth, with minor releases showing relatively consistent adoption across contexts while major and patch releases vary significantly by ecosystem and package size. In addition, while semantic versioning correlates with the technical complexity of the change-set, complexity itself shows minimal correlation with downstream adoption, suggesting that versioning signals rather than technical change drive dependency growth. Overall, while semantic versioning release information can be used as a unit of innovation in OSS development complementary to common sources for innovation metrics (e.g. scientific publications, patents, and standards), this measure should be weighted by ecosystem culture, package maturity, and release type to accurately capture innovation dynamics. We conclude with a discussion of the theoretical and practical implications of this novel measure of software innovation as well as future research directions.