mlsanityData to 7 Oct 2026

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

arXiv 2501.11264 ↗First posted 20 Jan 2025cs.SE cs.AI cs.CLcited by 4

Not on the map. Why?

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 20 Jan 2025.

Builds on

15 of 61 references.

References · 15

  1. 2023A Survey of Large Language Modelscited by 2,537
  2. 2022Do Users Write More Insecure Code with AI Assistants?cited by 186
  3. 2023Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issuescited by 78
  4. 2024Human-In-the-Loop Software Development Agentscited by 42
  5. 2023A3Test: Assertion-Augmented Automated Test Case Generationcited by 33
  6. 2023Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Studycited by 24
  7. 2024Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automationcited by 23
  8. 2023Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmapscited by 19
  9. 2023AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilitiescited by 17
  10. 2022How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilotcited by 14
  11. 2022Syntax-Aware On-the-Fly Code Completioncited by 10
  12. 2024PyTester: Deep Reinforcement Learning for Text-to-Testcase Generationcited by 6
3 more
  1. 2019An Empirical Study of the Relationships between Code Readability and Software Complexitycited by 3
  2. 2021On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programmingcited by 2
  3. 2023Students' Perspective on AI Code Completion: Benefits and Challengescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineeringcited by 2
  2. 2026Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Codecited by 1
  3. 2026Towards the Readability of LLM-Generated Codes through Multitask Representation Engineeringcited by 0
  4. 2026AI Writes Code, Humans Pay the Debt. An Empirical Study on the Sustainability and Evolution of Agent-Generated Codecited by 0