# Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report

arXiv: https://arxiv.org/abs/2408.10758 · HTML: https://mlsanity.com/paper/2408.10758/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs](https://mlsanity.com/paper/2502.19411/) (2025)
- [Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era](https://mlsanity.com/paper/2502.15287/) (2025)

## Abstract (arXiv)

[Background/Context] AI assistants like GitHub Copilot are transforming software engineering; several studies have highlighted productivity improvements. However, their impact on code quality, particularly in terms of maintainability, requires further investigation. [Objective/Aim] This study aims to examine the influence of AI assistants on software maintainability, specifically assessing how these tools affect the ability of developers to evolve code. [Method] We will conduct a two-phased controlled experiment involving professional developers. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants. We will employ Bayesian analysis to evaluate differences in completion time, perceived productivity, code quality, and test coverage.