Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability

Abstract: [Context] AI assistants, like GitHub Copilot and Cursor, are transforming software engineering. While several studies highlight productivity improvements, their impact on maintainability requires further investigation. [Objective] This study investigates whether co-development with AI assistants affects software maintainability, specifically how easily other developers can evolve the resulting source code. [Method] We conducted a two-phase controlled experiment involving 151 participants, 95% of whom were professional developers. In Phase 1, participants added a new feature to a Java web application, with or without AI assistance. In Phase 2, a randomized controlled trial, new participants evolved these solutions without AI assistance. [Results] AI-assisted development in Phase 1 led to a modest speedup in subsequent evolution and slightly higher average CodeHealth. Although neither difference was significant overall, the increase in CodeHealth was statistically significant when habitual AI users completed Phase 1. For Phase 1, we also observed a significant effect that corroborates previous productivity findings: using an AI assistant yielded a 30.7% median decrease in task completion time. Moreover, for habitual AI users, the mean speedup was 55.9%. [Conclusions] Our study adds to the growing evidence that AI assistants can effectively accelerate development. Moreover, we did not observe warning signs of degraded code-level maintainability. We recommend that future research focus on risks such as code bloat from excessive code generation and the build-up of cognitive debt as developers invest less mental effort during implementation.