Over-editing refers to a model modifying code beyond what is necessary (nrehiew.github.io)

Article summary

The article discusses the problem of over-editing in AI-assisted coding, where models modify code beyond what is necessary to fix a bug. This can lead to unnecessary changes, making code review more difficult. The author investigates whether existing language models have a tendency to over-edit and whether training models to be more faithful editors is possible. The study finds that some models, such as GPT-5.4, over-edit more than others, and that prompting models to preserve the original code can improve their editing behavior.

Main themes

• AI-assisted coding
• Over-editing
• Code review
• Model training
• Refactoring
• Technical debt

What commenters say

• Some commenters agree that over-editing is a significant problem in AI-assisted coding, making code review more difficult and potentially introducing unnecessary changes.
• Others argue that refactoring as you go is a good practice, but it requires careful consideration of when to refactor and when to leave the code alone.
• A few commenters suggest that the key to successful AI-assisted coding is to use the AI as a tool, rather than letting it drive the development process, and to carefully review its output.
• There is disagreement about the effectiveness of current AI models in writing non-trivial products, with some arguing that they are not yet smart enough to do so without careful supervision.
• Some commenters believe that the benefits of using AI-assisted coding tools, such as increased development speed, outweigh the potential drawbacks, while others prefer to write code by hand to maintain control and understanding.
• The importance of understanding the code and being able to review the AI's output is emphasized, as well as the need for AI models to be able to calibrate themselves to the project context.