Atlassian Research: What Do Developers Think About Code Readability in the Age of LLMs?

Atlassian Research: What Do Developers Think About Code Readability in the Age of LLMs?

As LLM-powered agents like Atlassian’s RovoDev start writing more of our code, a timely question emerges: Does code readability still matter when RovoDev Agents is doing the heavy lifting?

Introduction: Why Code Readability Still Matters in the Age of LLM Agents

In 2025, the buzz around Large Language Models (LLMs) and Agentic AI for Software Engineering is everywhere. From SWE chatbots (e.g., RovoDev CLI) to autonomous workflow agents (e.g., RovoDev Coding Agent), these technologies are transforming how we build and maintain software. But as LLM-powered agents like Atlassian’s RovoDev start writing more of our code, a timely question emerges: Does code readability still matter when RovoDev Agents is doing the heavy lifting?

Atlassian’s latest research, “Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian,” accepted at the 41th International Conference on Software Maintenance and Evolution (ICSME’25, Auckland, New Zealand, a prestigious conference in software engineering) dives deep into this question. This research is in collaboration with Associate Professor Kla Tantithamthavorn from Monash University. The findings are both surprising and reassuring for anyone worried about a future where humans and AI agents collaborate on code.


Meet RovoDev Coding Agent

RovoDev Coding Agent is Atlassian’s answer to collaborative, AI-assisted software development. Instead of aiming for full automation, RovoDev is designed to work alongside engineers—embedded in Jira, guiding tasks from planning to coding, always keeping a human in the loop for critical decisions.

How RovoDev fits into your workflow:

  1. Set Context: Select a Jira issue and code repo.
  2. Planning: The agent drafts a coding plan, which you can refine.
  3. Coding: RovoDev generates code, validated by tools and human review.
  4. Pull Request: Code is submitted for standard team review.

Why Investigate Code Readability in the Age of LLMs?

Readable code is the backbone of maintainable software. It’s easier to review, debug, and extend—especially in collaborative environments. As Guido van Rossum, creator of Python, famously said: “Code is read more often than it is written.”

But with LLMs generating more code, we wanted to know:


What Developers Think: Survey Insights

We surveyed 118 practitioners across Atlassian and the industry. Here’s what we learned:

Figure 1: The survey results about the importance, benefits, challenges of the code readability in the age of large language models (LLMs).

Human vs. LLM-Generated Code: What the Data Shows

To move beyond opinions, Atlassian ran an empirical study to investigate the readability of human-written code and RovoDev-generated code (powered by GPT-4) across 144 real Jira issues and 250 files in six programming languages.

How was readability measured?

Key findings: LLM Agents Can Write Readable Code

Bottom line: LLM Agents like RovoDev can produce code that teams can trust and maintain—key for scaling SWE Agents in the enterprise.

Figure 2: A comparison of various code readability measures between
human-written code and RovoDev-generated code.

Key Takeaways for Developers


Final Thoughts

Atlassian’s RovoDev is a glimpse into the future of software development—one where LLM Agents work hand-in-hand with people. The research is clear: readable code is here to stay, and with the right approach, LLM can help us write it.

Curious to learn more? Check out the original RovoDev blog and Atlassian’s RovoDev CLI product.

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