The advent to exascale requires more scalable and efficient techniques to help developers to locate, analyze and correct errors in parallel applications. PARallel COntrol flow Anomaly CHecker (PARCOACH) is a framework that detects the origin of collective errors in applications using MPI and/or OpenMP. In MPI, such errors include collective operations mismatches. In OpenMP, a collective error can be a barrier not called by all tasks in a team. In this paper, we present an extension of PARCOACH which improves its collective errors detection. We show our analysis is more precise and accurate than the previous one on different benchmarks and real applications.