Here's all the ways pitest will mutate your code to verify tests:
miniblog.
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https://tigerbeetle.com/blog/2025-02-27-why-we-designed-tigerbeetles-docs-from-scratch/ has an interesting distinction between "physical" and "logical" hash of a tarball.
By storing the hash of the decompressed tarball contents (i.e. the logical hash), they can verify the validity of files without needing to keep the tarball around.
Highlight of #POPL yesterday: seeing some excellent discussions of Coq for verifying programming languages!
https://popl20.sigplan.org/details/PLMW-POPL-2020/3/Theorem-provers-are-a-P-L-researcher-s-best-friend
I came away with a much clearer sense of *how* you'd verify an optimising compiler.
Invited talk: Safety Verification for Deep Neural Networks: https://popl20.sigplan.org/details/VMCAI-2020-papers/22/Safety-and-Robustness-for-Deep-Learning-with-Provable-Guarantees
How do we verify that a DNN is robust to adversarial attacks? How do we quantify safety? This approach looks at image features (Sift) and verifies all perturbations within a region.