# More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests

arXiv: https://arxiv.org/abs/2601.21276 · HTML: https://mlsanity.com/paper/2601.21276/

## Key facts

- Cited by 7 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 8 of its 22 references that are in the map.
- First posted on arXiv 29 Jan 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, 8 of 8)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [SycEval: Evaluating LLM Sycophancy](https://mlsanity.com/paper/2502.08177/) (2025)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Code Representation Learning At Scale](https://mlsanity.com/paper/2402.01935/) (2024)
- [Dissecting Human and LLM Preferences](https://mlsanity.com/paper/2402.11296/) (2024)
- [Do Code Clones Matter?](https://mlsanity.com/paper/1701.05472/) (2017)

## Built on by (most cited first, 7 of 7)

- [Is Agent Code Less Maintainable Than Human Code?](https://mlsanity.com/paper/2606.21804/) (2026)
- [(Im)Paired Programming: Coding Agents Improve Productivity but Harm Understanding](https://mlsanity.com/paper/2607.26375/) (2026)
- [Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects](https://mlsanity.com/paper/2604.06373/) (2026)
- [Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration](https://mlsanity.com/paper/2606.19042/) (2026)
- [3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse](https://mlsanity.com/paper/2607.07980/) (2026)
- [Code-Poisoning Property Inference Attacks](https://mlsanity.com/paper/2607.15970/) (2026)
- [How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests](https://mlsanity.com/paper/2607.21832/) (2026)

## Abstract (arXiv)

Large Language Model (LLM) Agents are advancing quickly, with the increasing leveraging of LLM Agents to assist in development tasks such as code generation. While LLM Agents accelerate code generation, studies indicate they may introduce adverse effects on development. However, existing metrics solely measure pass rates, failing to reflect impacts on long-term maintainability and readability, and failing to capture human intuitive evaluations of PR. To increase the comprehensiveness of this problem, we investigate and evaluate the characteristics of LLM to know the pull requests' characteristics beyond the pass rate. We observe the code quality and maintainability within PRs based on code metrics to evaluate objective characteristics and developers' reactions to the pull requests from both humans and LLM's generation. Evaluation results indicate that LLM Agents frequently disregard code reuse opportunities, resulting in higher levels of redundancy compared to human developers. In contrast to the quality issues, our emotions analysis reveals that reviewers tend to express more neutral or positive emotions towards AI-generated contributions than human ones. This disconnect suggests that the surface-level plausibility of AI code masks redundancy, leading to the silent accumulation of technical debt in real-world development environments. Our research provides insights for improving human-AI collaboration.