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, preregistered 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 Phase 2 revealed no significant differences in subsequent evolution with respect to completion time or code quality. Bayesian analysis suggests that any speed or quality improvements from AI use were at most small and highly uncertain. Observational results from Phase 1 corroborate prior research: using an AI assistant yielded a 30.7% median reduction in completion time, and habitual AI users showed an estimated 55.9% speedup. Conclusions Overall, we did not detect systematic maintainability advantages or disadvantages when other developers evolved code co-developed with AI assistants. Within the scope of our tasks and measures, we observed no consistent warning signs of degraded code-level maintainability. Future work should examine risks such as code bloat from excessive code generation and cognitive debt as developers offload more mental effort to assistants.

Carefully selecting the right collection datastructure can significantly improve the performance of a Java program. Unfortunately, the performance impact of a certain collection selection can be hard to estimate. To assist developers, there exist tools that recommend collections to use based on static and/or dynamic information about a program. The majority of existing collection selection tools for Java pick their selections dynamically, which means that they must trade off sophistication in their selection algorithm against its run time overhead. For static collection selection, the Brainy tool has demonstrated that complex, machine-dependent models can produce substantial performance improvements, albeit only for C++ so far.