  We introduce a novel approach for inferring natural preconditions from code.
Our technique produces preconditions of high quality in terms of both
correctness (modulo a test generator) and naturalness. Prior works generate
preconditions from scratch through combinations of boolean predicates, but fall
short in readability and ease of comprehension. Our innovation lies in,
instead, leveraging the structure of a target method as a seed to infer a
precondition through program transformations. Our evaluation shows that humans
can more easily reason over preconditions inferred using our approach. Lastly,
we instantiate our technique into a framework which can be applied at scale. We
present a dataset of ~18k Java (method, precondition) pairs obtained by
applying our framework to 87 real-world projects. We use this dataset to both
evaluate our approach and draw useful insights for future research in
precondition inference.
