# Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian

arXiv: https://arxiv.org/abs/2501.11264 · HTML: https://mlsanity.com/paper/2501.11264/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues](https://mlsanity.com/paper/2307.12596/) (2023)
- [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024)
- [A3Test: Assertion-Augmented Automated Test Case Generation](https://mlsanity.com/paper/2302.10352/) (2023)
- [Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Study](https://mlsanity.com/paper/2311.11177/) (2023)
- [Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation](https://mlsanity.com/paper/2402.00905/) (2024)
- [Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps](https://mlsanity.com/paper/2309.04142/) (2023)
- [AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities](https://mlsanity.com/paper/2305.16615/) (2023)
- [How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilot](https://mlsanity.com/paper/2208.14613/) (2022)
- [Syntax-Aware On-the-Fly Code Completion](https://mlsanity.com/paper/2211.04673/) (2022)
- [PyTester: Deep Reinforcement Learning for Text-to-Testcase Generation](https://mlsanity.com/paper/2401.07576/) (2024)
- [An Empirical Study of the Relationships between Code Readability and Software Complexity](https://mlsanity.com/paper/1909.01760/) (2019)
- [On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming](https://mlsanity.com/paper/2110.15246/) (2021)
- [Students' Perspective on AI Code Completion: Benefits and Challenges](https://mlsanity.com/paper/2311.00177/) (2023)

## Built on by (most cited first, 4 of 4)

- [From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineering](https://mlsanity.com/paper/2512.23844/) (2025)
- [Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code](https://mlsanity.com/paper/2605.13280/) (2026)
- [Towards the Readability of LLM-Generated Codes through Multitask Representation Engineering](https://mlsanity.com/paper/2606.06214/) (2026)
- [AI Writes Code, Humans Pay the Debt. An Empirical Study on the Sustainability and Evolution of Agent-Generated Code](https://mlsanity.com/paper/2609.04208/) (2026)

## Abstract (arXiv)

Software engineers spend a significant amount of time reading code during the software development process, especially in the age of large language models (LLMs) that can automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from practitioners' perspectives in this new era. In this paper, we conduct a survey to explore the practitioners' perspectives on code readability in the age of LLMs and investigate the readability of our LLM-based software development agents framework, HULA, by comparing its generated code with human-written code in real-world scenarios. Overall, the findings underscore that (1) readability remains a critical aspect of software development; (2) the readability of our LLM-generated code is comparable to human-written code, fostering the establishment of appropriate trust and driving the broad adoption of our LLM-powered software development platform.