# On the Adoption of AI Coding Agents in Open-source Android and iOS Development

arXiv: https://arxiv.org/abs/2602.12144 · HTML: https://mlsanity.com/paper/2602.12144/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 4 of 4)

- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows](https://mlsanity.com/paper/2507.08149/) (2025)
- [An Empirical Study of Challenges in Machine Learning Asset Management](https://mlsanity.com/paper/2402.15990/) (2024)
- [AppForge: From Assistant to Independent Developer -- Are GPTs Ready for Software Development?](https://mlsanity.com/paper/2510.07740/) (2025)

## Built on by (most cited first, 1 of 1)

- [Early Adoption of Agentic Coding Tools by GitHub Projects](https://mlsanity.com/paper/2607.14037/) (2026)

## Abstract (arXiv)

AI coding agents are increasingly contributing to software development, yet their impact on mobile development has received little empirical attention. In this paper, we present the first category-level empirical study of agent-generated code in open-source mobile app projects. We analyzed PR acceptance behaviors across mobile platforms, agents, and task categories using 2,901 AI-authored pull requests (PRs) in 193 verified Android and iOS open-source GitHub repositories in the AIDev dataset. We find that Android projects have received 2x more AI-authored PRs and have achieved higher PR acceptance rate (71%) than iOS (63%), with significant agent-level variation on Android. Across task categories, PRs with routine tasks (feature, fix, and ui) achieve the highest acceptance, while structural changes like refactor and build achieve lower success and longer resolution times. Furthermore, our evolution analysis shows improvement in PR resolution time on Android through mid-2025 before it declined again. Our findings offer the first evidence-based characterization of AI agents effects on OSS mobile projects and establish empirical baselines for evaluating agent-generated contributions to design platform aware agentic systems.