PoudlardoLeanstral: Open-source agent for trustworthy coding and formal proof engineering
Lean 4 paper (2021): https://dl.acm.org/doi/10.1007/978-3-030-79876-5_37
The real world success they report reminds me of Simon Willison’s Red Green TDD: https://simonwillison.net/guides/agentic-engineering-pattern...
> Instead of taking a stab in the dark, Leanstral rolled up its sleeves. It successfully built test code to recreate the failing environment and diagnosed the underlying issue with definitional equality. The model correctly identified that because def creates a rigid definition requiring explicit unfolding, it was actively blocking the rw tactic from seeing the underlying structure it needed to match.
If Agent is writing the tests itself, does it offer better correctness guarantees than letting it write code and tests?
It is definitely not foolproof but IMHO, to some extent, it is easier to describe what you expect to see than to implement it so I don't find it unreasonable to think it might provide some advantages in terms of correctness.
That definitely depends upon the situation. More often than not, properly testing a component takes me more time than writing it.
In my experience, this tends to be more related to instrumentation / architecture than a lack of ability to describe correct results. TDD is often suggested as a solution.
Given the issues with AWS with Kiro and Github, We already have just a few high-profile examples of what happens when AI is used at scale and even when you let it generate tests which is something you should absolutely not do.
Otherwise in some cases, you get this issue [0].
[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...
The linked article does not speak of tests, it speaks of a team that failed to properly review an LLM refactor then proceeds to blame the tooling.
LLMs are good at writing tests in my experience.
In my experience the agent regularly breaks some current features while adding a new one - much more often than a human would. Agents too often forget about the last feature when adding the next and so will break things. Thus I find Agent generated tests important as they stop the agent from making a lot of future mistakes.