@inproceedings{bastani2018active,
author = {Bastani, Osbert and Sharma, Rahul and Aiken, Alex and Liang, Percy},
title = {Active learning of points-to specifications},
organization = {ACM},
booktitle = {PLDI},
year = {2018},
month = {June},
abstract = {When analyzing programs, large libraries pose significant
challenges to static points-to analysis. A popular solution
is to have a human analyst provide points-to specifications
that summarize relevant behaviors of library code, which
can substantially improve precision and handle missing code
such as native code. We propose Atlas, a tool that automatically infers points-to specifications. Atlas synthesizes unit
tests that exercise the library code, and then infers points-to
specifications based on observations from these executions.
Atlas automatically infers specifications for the Java standard library, and produces better results for a client static
information flow analysis on a benchmark of 46 Android
apps compared to using existing handwritten specifications.},
url = {http://approjects.co.za/?big=en-us/research/publication/active-learning-of-points-to-specifications/},
}