# How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilot

arXiv: https://arxiv.org/abs/2208.14613 · HTML: https://mlsanity.com/paper/2208.14613/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2022.

## Built on by (most cited first, 14 of 14)

- ["I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust](https://mlsanity.com/paper/2405.00623/) (2024)
- [RepoFusion: Training Code Models to Understand Your Repository](https://mlsanity.com/paper/2306.10998/) (2023)
- [NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness](https://mlsanity.com/paper/2401.15963/) (2024)
- [PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback](https://mlsanity.com/paper/2412.03578/) (2024)
- [Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions](https://mlsanity.com/paper/2302.07248/) (2023)
- [Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming](https://mlsanity.com/paper/2306.05153/) (2023)
- [Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers](https://mlsanity.com/paper/2403.15600/) (2024)
- [BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks](https://mlsanity.com/paper/2404.07387/) (2024)
- [R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents](https://mlsanity.com/paper/2303.00732/) (2023)
- [Can ChatGPT Pass An Introductory Level Functional Language Programming Course?](https://mlsanity.com/paper/2305.02230/) (2023)
- [Human-AI Experience in Integrated Development Environments: A Systematic Literature Review](https://mlsanity.com/paper/2503.06195/) (2025)
- [Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software](https://mlsanity.com/paper/2504.15549/) (2025)
- [Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian](https://mlsanity.com/paper/2501.11264/) (2025)
- [Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects](https://mlsanity.com/paper/2502.02368/) (2025)

## Abstract (arXiv)

Background: Recent advancements in large language models have motivated the practical use of such models in code generation and program synthesis. However, little is known about the effects of such tools on code readability and visual attention in practice. Objective: In this paper, we focus on GitHub Copilot to address the issues of readability and visual inspection of model generated code. Readability and low complexity are vital aspects of good source code, and visual inspection of generated code is important in light of automation bias. Method: Through a human experiment (n=21) we compare model generated code to code written completely by human programmers. We use a combination of static code analysis and human annotators to assess code readability, and we use eye tracking to assess the visual inspection of code. Results: Our results suggest that model generated code is comparable in complexity and readability to code written by human pair programmers. At the same time, eye tracking data suggests, to a statistically significant level, that programmers direct less visual attention to model generated code. Conclusion: Our findings highlight that reading code is more important than ever, and programmers should beware of complacency and automation bias with model generated code.