![[2507.00788] Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2507.00788] Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability
Summary
This study investigates the impact of AI assistants on software maintainability, revealing no significant differences in code evolution despite productivity gains during development.
Why It Matters
As AI assistants become integral to software engineering, understanding their effects on maintainability is crucial for developers and organizations. This research provides insights into whether reliance on AI tools compromises the long-term quality of code, which is vital for sustainable software development.
Key Takeaways
- AI assistants can significantly reduce development time but may not enhance maintainability.
- No systematic maintainability advantages were observed in code developed with AI assistance.
- Future research should address potential risks like code bloat and cognitive debt associated with AI use.
Computer Science > Software Engineering arXiv:2507.00788 (cs) [Submitted on 1 Jul 2025 (v1), last revised 26 Feb 2026 (this version, v3)] Title:Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability Authors:Markus Borg, Dave Hewett, Nadim Hagatulah, Noric Couderc, Emma Söderberg, Donald Graham, Uttam Kini, Dave Farley View a PDF of the paper titled Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability, by Markus Borg and 7 other authors View PDF HTML (experimental) 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] 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 improveme...
