Learning Commutativity Specifications

Timon Gehr, Dimitar K. Dimitrov, Martin T. Vechev

Abstract: In this work we present a new sampling-based “black box” inference approach for learning the behaviors of a library component. As an application, we focus on the problem of automatically learning commutativity specifications of data structures. This is a very challenging problem, yet important, as commutativity specifications are fundamental to program analysis, concurrency control and even lower bounds.