An Empirical Study of Code Clone Genealogies in Human–AI Collaborative Development

Code clones consist of two or more identical or similar code snippets. Code clones hurt maintainability by requiring synchronized updates across multiple locations and increasing the risk of inconsistent changes. To understand how clones evolve, the genealogy of code clones captures the evolutionary history of duplicated code snippets by linking them across successive versions of the software system. Since the emergence of Large Language Models (LLMs), software engineering has been reshaped, with code written and evolved differently. This evolution has given rise to coding agents who act as partners to developers. While code clone genealogy is well understood in human-centric development, its evolution in human–agent collaborative projects remains unclear. In this study, we analyze 350 code clone lineages across 6 software projects in which human actively colaborate with coding agents. We observed that humans introduce 85.71% of code clones, whereas agents contribute only 14.29%. Despite similar clone survival rates for both humans (80%) and agents (76%), the maintenance dynamics differ significantly. The analysis of genealogies reveals that humans predominate in maintaining lineages created by agents. These findings highlight that humans remain critical for the evolution of code generated by coding agents.