Quantitative Strongest Post
Abstract
We present a novel strongest-postcondition-style calculus for quantitative reasoning about non-deterministic programs with loops. Whereas existing quantitative weakest pre allows reasoning about the value of a quantity after a program terminates on a given initial state, quantitative strongest post allows reasoning about the value that a quantity had before the program was executed and reached a given final state. We show how strongest post enables reasoning about the flow of quantitative information through programs. Similarly to weakest liberal preconditions, we also develop a quantitative strongest liberal post. As a byproduct, we obtain the entirely unexplored notion of strongest liberal postconditions and show how these foreshadow a potential new program logic - partial incorrectness logic - which would be a more liberal version of O'Hearn's recent incorrectness logic.
Keywords
Cite
@article{arxiv.2202.06765,
title = {Quantitative Strongest Post},
author = {Linpeng Zhang and Benjamin Lucien Kaminski},
journal= {arXiv preprint arXiv:2202.06765},
year = {2022}
}
Comments
Full version of a conference paper presented at OOPSLA 2022