Nowadays, with the rise of Copilot and ChatGPT, and other AI-powered code refactoring tools, we should be more efficient, but is it, but is it saving us time and improving software quality? Even though AI refactoring gives us the promise that we now have less grunt work, it introduces risks for software developers in terms of quality, control, and unintended consequences. Often it is necessary to clean up the mess of a Copilot refactoring, or clarify the error to ChatGPT multiple times, while being drowned in word vomit code. It generates so much code, it is hard to see the small changes.
Furthermore, a contact in a large Copenhagen-based enterprise gave us the following insight into their troubles with AI:
“The quality of code of junior developers has dropped by 54% in the years since the launch of ChatGPT”.
This contact has asked to remain anonymous, and that should key us into another portion of the issue at hand:
Companies don’t want to admit when they are using AI to write code.
Well why not?
The Problem with AI Training Data and Stochastic Systems
We all know that AI models are trained on vast amounts of data and code, but even though the AI may understand how to predict code, we don’t know whether the training set of code was actually any good.
There is a lack of transparency on which code it was trained on, and it is very likely that a large amount of the code is either legacy, not functional or just plain badly written code.
As the age-old adage goes: Garbage in, garbage out, if the AI just keeps recycling garbage from inconsistent or outdated codebases, it can risk introducing suboptimal patterns into refactored code.
Basically, even though our code should be refactored, it still incomprehensible how useful or efficient the refactoring was. It has been shown that Copilot and other AI models sometimes produce error-prone or insecure code snippets📌 GitHub Copilot & Security Concerns
We need a non-AI-controlled approach that keeps developers in charge.
AI will always lack context – either through a context window that is too small, or by missing out on some bigger picture – thereby creating unintended consequences.
While AI can predict isolated improvements, but it doesn’t see the entire codebase or long-term impact, and it completely fails at multi-repo tasks. Refactoring isn’t just about “rewriting” code - it’s about understanding its role within the entire system.
Thought Experiment:
I’d like you to join in a thought experiment with me. Imagine I hand you a book, and ask you to memorize its contents. So you do. You take the book and read it again and again until all the information is safely locked away in your memory, and you feel confident to answer any question asked about the contents. Later, I come back and ask:
“From what tree was the paper of that book made?”
You would not and could not ever know the answer to this question by only examining the code, and that’s the exact issue with using AI for architectural tasks: AI lacks context about the entire system that doesn’t exist in the code in any practical or meaningful sense.
Refactoring Without Full Visibility May Lead To:
- Breaking dependencies
- Removing "unused" but necessary functions
- Performance regressions
And this is just the functional side.
Business Logic in Large Systems
AI does not understand your organization’s logic which leads to subtle regressions of your user flows. Edge cases may not be considered and can break things in unexpected ways, hurting user trust in your products and brand.
Refactoring without context is like moving furniture around in a dark room - you might trip over something critical.
We all, as developers, rely on understanding the code structure for debugging, to be able to improve and to be able to maintain the codebase. The issue is that with AI refactoring is that we don’t know why it made changes.
This makes debugging harder, as logic and intent are not always translated well. AI refactoring is like “word vomit” in code - how can you filter essential changes from unnecessary noise?
Also, AI might change too much at once, which leads to overcomplicated logic, unexpected behavior and performance regression.
This means that developers lose control over incremental, intentional improvements.
For example, AI-based refactoring may remove necessary but "unused" functions that were crucial for future updates. AI refactoring is like a junior dev on autopilot - excited to rewrite everything, but unaware of long-term consequences. The goal should be that AI should augment and aid developer decision-making, instead of replacing it.
Overall, AI refactoring introduces risks due to lack of quality control, context, and developer insight. What is critical is that developers need full visibility and incremental changes to ensure code maintainability.
The Alternative
You probably expected this, but here is the part where I suggest another solution: NanoAPI. A developer tool designed to help teams understand, refactor and modularize their codebases without the pain of major rewrites. Because of our CLI and UI, NanoAPI helps to identify dead endpoints in code, detect bottlenecks, and generate system-level interaction maps.
In comparison with traditional refactoring tools, NanoAPI gives developers full visibility and control over their architecture, making refactoring easier, safer, and more efficient for everyone.
Here is a list of the core features at the time of writing:
✅ Multi-language support (Python, C#, with more languages on the roadmap)
✅ Auto-generated diagrams of system- and code-level interactions to pinpoint tech debt, bottlenecks, and the risk levels of refactors on various parts of the system.
✅ Deeper insights into code written by AI or former employees.
✅ Git history-style exploration of your architecture over time.
✅ KPIs and insights into how your technology transformations are progressing.
And we have so much more on the way.
If your organization is suffering from these problems, please reach out to us via 📧email or on https://nanoapi.io.
We’re building this tool not only to put a collar on AI-refactoring, but also to build trust with developers. That’s why we’re source-available first. Please check out the project on GitHub and star us⭐!
Top comments (0)