We present set-based pre-analysis: a virtually universal op-
timization technique for flow-insensitive points-to analysis.
Points-to analysis computes a static abstraction of how ob-
ject values flow through a program’s variables. Set-based
pre-analysis relies on the observation that much of this rea-
soning can take place at the set level rather than the value
level. Computing constraints at the set level results in sig-
nificant optimization opportunities: we can rewrite the in-
put program into a simplified form with the same essential
points-to properties. This rewrite results in removing both
local variables and instructions, thus simplifying the sub-
sequent value-based points-to computation. E
ectively, set-
based pre-analysis puts the program in a normal form opti-
mized for points-to analysis.
Compared to other techniques for o
-line optimization of
points-to analyses in the literature, the new elements of our
approach are the ability to eliminate statements, and not just
variables, as well as its modularity: set-based pre-analysis
can be performed on the input just once, e.g., allowing the
pre-optimization of libraries that are subsequently reused
many times and for di
erent analyses. In experiments with
Java programs, set-based pre-analysis eliminates 30% of the
program’s local variables and 30% or more of computed
context-sensitive points-to facts, over a wide set of bench-
marks and analyses, resulting in a
20% average speedup
(max: 110%, median: 18%)
