  Previous work has shown that one can often greatly speed up static analysis
by computing data flows not for every edge in the program's control-flow graph
but instead only along definition-use chains. This yields a so-called sparse
static analysis. Recent work on SparseDroid has shown that specifically taint
analysis can be "sparsified" with extraordinary effectiveness because the taint
state of one variable does not depend on those of others. This allows one to
soundly omit more flow-function computations than in the general case.
  In this work, we now assess whether this result carries over to the more
generic setting of so-called Interprocedural Distributive Environment (IDE)
problems. Opposed to taint analysis, IDE comprises distributive problems with
large or even infinitely broad domains, such as typestate analysis or linear
constant propagation. Specifically, this paper presents Sparse IDE, a framework
that realizes sparsification for any static analysis that fits the IDE
framework.
  We implement Sparse IDE in SparseHeros, as an extension to the popular Heros
IDE solver, and evaluate its performance on real-world Java libraries by
comparing it to the baseline IDE algorithm. To this end, we design, implement
and evaluate a linear constant propagation analysis client on top of
SparseHeros. Our experiments show that, although IDE analyses can only be
sparsified with respect to symbols and not (numeric) values, Sparse IDE can
nonetheless yield significantly lower runtimes and often also memory
consumptions compared to the original IDE.
