Modular Static Analysis of Actor Programs

Existing static analyses for actor programs explicitely model all possible execution interleavings. Because the number of interleavings increases exponentially with the number of actors and messages exchanged, such analyses scale poorly. We describe the first modular analysis for actor programs, that analyzes each actor in separation of each other. This analysis over-approximate over the diferent interleavings without explicitly modeling them, rendering it scalable. We demonstrate its enhanced scalability by comparing the analysis of the Savina benchmark suite with a non-modular analysis and our new modular analysis. Our technique succeeds in analyzing all of the Savina benchmarks in a matter of seconds, while the non-modular analysis times out on more than half of these benchmarks. Moreover, we show that the precision of our modular remains on par with the precision of the non-modular analysis.