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This well-thought article restricts the notion of code review to pull request assessment - diff file - on an existing code base, perhaps a mostly human-written legacy code base.

As a Java code generation researcher, I would appreciate any constructive comments regarding the automated code review of automated changes made to an entirely generated code base, whose live specifications and requirements have been modified in order to generate the code being reviewed.




One nit-pick with the article:

It links to a Forbes article arguing that the best chess is played by humans and computers working together.

This concept is evidently false. Alpha Zero played super human chess without any human assistance beyond coding its machine learning algorithm and inputting the rules of chess.

Furthermore, in a timed chess match the delay and possible misjudgments from a human would detract from the performance of the human-computer team versus the computer chess program alone.


Fascinating - are you imagining a sort of adversarial AI situation, where one LLM bot writes the PRs, and a different one reviews them, leading to an organically improving codebase? Kind of a cool idea.


Pull requests are too-downstream - a legacy I believe.

An AGI system that self-improves its code will regenerate every component impacted by the enhancement starting from live system design narratives, useful existing components, relevant design patterns, and intermediate development artifacts that are discarded or become stale in human-driven legacy coding.

I see "agents" as mostly bodies of assembled prompts for LLMs of various strengths used at the appropriate time in the pipeline of code development. A code review agent's prompt would not have the task of generating code and thus not need all that particular context, but would look for historically observed 'gotchas' and flag those for automatic repair, and the repair could go all the way back through the artifact chain to the text requirements and specifications.





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