# Is LLM-Generated Code More Maintainable & Reliable than Human-Written Code?

arXiv: https://arxiv.org/abs/2508.00700 · HTML: https://mlsanity.com/paper/2508.00700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2025.

## Built on by (most cited first, 5 of 5)

- [Evaluating Language Models on Cross-Language Code Functional Equivalence](https://mlsanity.com/paper/2608.23961/) (2026)
- [AnCoder: Anchored Code Generation via Discrete Diffusion Models](https://mlsanity.com/paper/2602.17688/) (2026)
- [Needle in the Repo: A Benchmark for Maintainability in AI-Generated Repository Edits](https://mlsanity.com/paper/2603.27745/) (2026)
- [Quality and Security Signals in AI-Generated Python Refactoring Pull Requests](https://mlsanity.com/paper/2605.21453/) (2026)
- [When Who You Are Can Change the Code You Get: A Study of Persona-Induced Bias in LLM Code Generation](https://mlsanity.com/paper/2609.22102/) (2026)

## Abstract (arXiv)

Background: The rise of Large Language Models (LLMs) in software development has opened new possibilities for code generation. Despite the widespread use of this technology, it remains unclear how well LLMs generate code solutions in terms of software quality and how they compare to human-written code. Aims: This study compares the internal quality attributes of LLM-generated and human-written code. Method: Our empirical study integrates datasets of coding tasks, three LLM configurations (zero-shot, few-shot, and fine-tuning), and SonarQube to assess software quality. The dataset comprises Python code solutions across three difficulty levels: introductory, interview, and competition. We analyzed key code quality metrics, including maintainability and reliability, and the estimated effort required to resolve code issues. Results: Our analysis shows that LLM-generated code has fewer bugs and requires less effort to fix them overall. Interestingly, fine-tuned models reduced the prevalence of high-severity issues, such as blocker and critical bugs, and shifted them to lower-severity categories, but decreased the model's performance. In competition-level problems, the LLM solutions sometimes introduce structural issues that are not present in human-written code. Conclusion: Our findings provide valuable insights into the quality of LLM-generated code; however, the introduction of critical issues in more complex scenarios highlights the need for a systematic evaluation and validation of LLM solutions. Our work deepens the understanding of the strengths and limitations of LLMs for code generation.