Indexing Context-Sensitive Reachability

TL;DR

This paper introduces extsc{Flare}, a novel approach that reduces CFL reachability to graph reachability, enabling scalable, fast context-sensitive data flow analysis for large software systems.

Contribution

The paper presents extsc{Flare}, a reduction technique leveraging reachability indexing to improve scalability and efficiency of context-sensitive data flow analysis.

Findings

Achieves orders of magnitude speedup in analysis

Uses almost linear space for reachability indexes

Effective on large-scale software benchmarks

Abstract

Many context-sensitive data flow analyses can be formulated as a variant of the all-pairs Dyck-CFL reachability problem, which, in general, is of sub-cubic time complexity and quadratic space complexity. Such high complexity significantly limits the scalability of context-sensitive data flow analysis and is not affordable for analyzing large-scale software. This paper presents \textsc{Flare}, a reduction from the CFL reachability problem to the conventional graph reachability problem for context-sensitive data flow analysis. This reduction allows us to benefit from recent advances in reachability indexing schemes, which often consume almost linear space for answering reachability queries in almost constant time. We have applied our reduction to a context-sensitive alias analysis and a context-sensitive information-flow analysis for C/C++ programs. Experimental results on standard benchmarks and open-source software demonstrate that we can achieve orders of magnitude speedup at the cost of only moderate space to store the indexes. The implementation of our approach is publicly available.