Highly scalable distributed dataflow analysis

Dynamic dataflow analyses find software errors by tracking meta-values associated with a program's runtime data. Despite their benefits, the orders-of-magnitude slowdowns that accompany these systems limit their use to the development stage; few users would tolerate such overheads. This work extends dynamic dataflow analyses with a novel sampling system which ensures that runtime slowdowns do not exceed a user-defined threshold. While previous sampling methods are inadequate for dataflow analyses, our technique efficiently reduces the number and size of analyzed dataflows. In doing so, it allows individual users to test large, stochastically chosen sets of a process's dataflows. Large populations can therefore, in aggregate, analyze a larger portion of the program than is possible by any single user running a complete, but slow, analysis. In our experimental evaluation, we show that 1 out of every 10 users expose a number of security exploits while only experiencing a 10% performance slowdown, in contrast with the 100× overhead caused by a complete analysis that exposes the same problems.