Affiliation:
1. Georgia Institute of Technology, Atlanta, GA
2. University of Wisconsin-Madison, Madison, WI
Abstract
Many program-analysis problems can be formulated as graph-reachability problems. Interleaved Dyck language reachability (
InterDyck
-reachability) is a fundamental framework to express a wide variety of program-analysis problems over edge-labeled graphs. The
InterDyck
language represents an intersection of multiple matched-parenthesis languages (i.e., Dyck languages). In practice, program analyses typically leverage one Dyck language to achieve context-sensitivity, and other Dyck languages to model data dependencies, such as field-sensitivity and pointer references/dereferences. In the ideal case, an
InterDyck
-reachability framework should model multiple Dyck languages
simultaneously
.
Unfortunately, precise
InterDyck
-reachability is undecidable. Any practical solution must over-approximate the exact answer. In the literature, a lot of work has been proposed to over-approximate the
InterDyck
-reachability formulation. This article offers a new perspective on improving both the precision and the scalability of
InterDyck
-reachability: we aim at simplifying the underlying input graph
G
. Our key insight is based on the observation that if an edge is not contributing to any
InterDyck
-paths, we can safely eliminate it from
G
. Our technique is orthogonal to the
InterDyck
-reachability formulation and can serve as a pre-processing step with any over-approximating approach for
InterDyck
-reachability. We have applied our graph simplification algorithm to pre-processing the graphs from a recent
InterDyck
-reachability-based taint analysis for Android. Our evaluation of three popular
InterDyck
-reachability algorithms yields promising results. In particular, our graph-simplification method improves both the scalability and precision of all three
InterDyck
-reachability algorithms, sometimes dramatically.
Funder
Rajiv and Ritu Batra
Facebook under a Probability and Programming Research Award
Amazon under an Amazon Research Award
United States National Science Foundation
Defense Advanced Research Projects Agency
ONR
Facebook Graduate Fellowship
Publisher
Association for Computing Machinery (ACM)
Cited by
3 articles.
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