Multicore Environment State Representation for Agent-Directed Test Generation

A crucial step in the design of multicore systems is to validate the interaction between cores. This involves test program generation and runtime analysis. We propose a novel reinforcement learning approach to directed test generation, where an agent induces a suite of programs, which are executed in a simulation environment for a multicore. It focuses on how to recover state information from raw observations of the environment such that the agent can learn from interaction how to improve coverage for any verification task. We evaluated our state representation for different verification tasks involving 16 and 32-core ARMv8 2-level MOESI designs.