@inproceedings{brockschmidt2017learning,
author = {Brockschmidt, Marc and Chen, Yuxin and Kohli, Pushmeet and Krishna, Siddharth and Tarlow, Daniel},
title = {Learning Shape Analysis},
booktitle = {Proceedings of SAS'17},
year = {2017},
month = {July},
abstract = {We present a data-driven verification framework to automatically prove memory safety of heap-manipulating programs. Our core contribution is a novel statistical machine learning technique that maps observed program states to (possibly disjunctive) separation logic formulas describing the invariant shape of (possibly nested) data structures at relevant program locations. We then attempt to verify these predictions using a program verifier, where counterexamples to a predicted invariant are used as additional input to the shape predictor in a refinement loop.
We have implemented our techniques in Locust, an extension of the GRASShopper verification tool. Locust is able to automatically prove memory safety of implementations of classical heap-manipulating programs such as insertionsort, quicksort and traversals of nested data structures.},
publisher = {Springer},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-shape-analysis/},
edition = {Proceedings of SAS'17},
}