Workflow Automation in Open-Source Software Development: Accelerating Innovation Through Mechanization and Orchestration

This study develops a conceptual framework distinguishing two foundational mechanisms of workflow automation, mechanization and orchestration, and empirically examines how workflow automation accelerates innovation in the context of open-source software (OSS) development. Drawing on the process automation, innovation, and OSS development literature, we propose that (a) workflow automation accelerates OSS innovation and (b) distinct workflow automation mechanisms—mechanization and orchestration—differentially affect incremental innovation (i.e., maintenance) and substantive innovation (i.e., new development), respectively. Based on longitudinal observations of more than 4,500 GitHub repositories and 280,000 issues from 2019 to 2020, we combine machine learning with econometrics in an embedded interlayering multimethod design. To test our hypotheses, our econometric analyses further employ look-ahead rolling entry matching (LA-REM) in tandem with different estimation strategies, including difference-in-differences (DiD) and instrumental variables (IVs). Our analyses first estimate that workflow automation (specifically GitHub Actions) accelerates issue resolution by 10.1%, translating to 4.3 days saved per issue, demonstrating significant improvement in innovation speed. Second, particularly notably, we distinguish between mechanization and orchestration to further investigate how these workflow automation mechanisms differentially affect two types of innovation in software development. Our analyses uncover that mechanization significantly accelerates the incremental innovation of maintenance by 7.8%, saving an average of 3.0 days per issue; however, orchestration shows an inconsequential effect. In contrast, for the substantive innovation of new development, mechanization shows a limited effect, whereas orchestration accelerates issue resolution speed by 16.6%, saving an average of 9.1 days per issue. We further leverage counterfactual estimations to analyze the downstream effects of workflow automation. We observe that the speed gain does not compromise quality, as workflow automation significantly increases the contribution volume (closed issues) and project quality (stars, forks, and releases). This study unpacks the distinct mechanisms of workflow automation, showing that mechanization accelerates routine maintenance-oriented incremental innovation, whereas orchestration catalyzes creative development-oriented substantive innovation. Our study contributes to research and practice on automation, innovation, and software development by demonstrating that workflow automation not only accelerates innovation but also reshapes the underlying processes through which innovation unfolds in OSS development.

History: Gal Oestreicher-Singer, Senior Editor; Min-Seok Pang, Associate Editor.

Funding: This work was supported by OpenAI’s Researcher Access Program and the Google Cloud Researcher Award.

Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2024.1551.