The IFDS framework supports interprocedural dataflow analysis with distributive flow functions over finite domains. A large class of interprocedural dataflow analysis problems can be formulated as IFDS problems, and thus can be solved with the IFDS framework precisely. Unfortunately, scaling IFDS analysis to large-scale programs is challenging in terms of both high memory consumption and low analysis efficiency.

This paper presents DStream, a scalable system dedicated to precise and highly parallel IFDS analysis for large-scale programs. DStream leverages a streaming-based out-of-core computation model to greatly reduce memory usage and adopts fine-grained data-parallelism to achieve efficiency. We implemented a taint analysis as a DStream instance analysis and compared DStream with three state-of-the-art tools. Our experiments validate that DStream outperforms all other tools with average speedups from 6.16x to 22.30x on a commodity PC with limited available memory. Meanwhile, the experiments confirm that DStream successfully scales large-scale programs which FlowDroid and/or DiskDroid fail to analyze.