The Emerging Artifacts of Centralized Open-Code

Abstract

In 2022, generative model based coding assistants became widely available with the public release of GitHub Copilot. Approaches to generative coding are often critiqued within the context of advances in machine learning. We argue that tools such as Copilot are better understood when contextualized against technologies derived from the same communities and datasets. Our work traces the historical and ideological origins of free and open source code and characterizes the process of centralization. We examine three case studies - Dependabot, Crater, and Copilot - to compare the engineering, social, and legal qualities of technical artifacts derived from shared community-based labor. Our analysis focuses on the implications these artifacts create for infrastructural dependencies, community adoption, and intellectual property. Reframing generative coding assistants through a set of peer technologies broadens considerations for academics and policymakers beyond machine learning, to include the ways technical artifacts are derived from communities.