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Context- and flow-sensitive value-flow information is an important buildingblock for many static analysis tools. Unfortunately, current approaches tocompute value-flows do not scale to large codebases, due to high memory andruntime requirements. This paper proposes a new scalable approach to computevalue-flows via graph reachability. To this end, we develop a new graphstructure as an extension of LLVM IR that contains two additional operationswhich significantly simplify the modeling of pointer aliasing. Further, byprocessing nodes in the opposite direction of SSA def-use chains, we are ableto minimize the tree width of the resulting graph. This allows us to employefficient tree traversal algorithms in order to resolve graph reachability.  We present a value-flow analysis framework,DFI, implementing our approach. Wecompare DFI against two state-of-the-art value-flow analysis frameworks, Phasarand SVF, to extract value-flows from 4 real-world software projects. Given 32GBof memory, Phasar and SVF are unable to complete analysis of larger projectssuch as OpenSSL or FFmpeg, while DFI is able to complete all evaluations. Forthe subset of benchmarks that Phasar and SVF do handle, DFI requiressignificantly less memory (1.5% of Phasar's, 6.4% of SVF's memory footprint onaverage) and runs significantly faster (23x speedup over Phasar, 57x comparedto SVF). Our analysis shows that, in contrast to previous approaches, DFI'smemory and runtime requirements scale almost linearly with the number ofanalyzed instructions.

DFI: An Interprocedural Value-Flow Analysis Framework that Scales to Large Codebases