  Over the years of challenges on detecting the crash consistency of
non-volatile persistent memory (PM) bugs and developing new tools to identify
those bugs are quite stretching due to its inconsistent behavior on the file or
storage systems. In this paper, we evaluated an open-sourced automatic bug
detector tool (i.e. AGAMOTTO) to test NVM level hashing PM application to
identify performance and correctness PM bugs in the persistent (main) memory.
Furthermore, our faithful validation tool able to discovered 65 new NVM level
hashing bugs on PMDK library and it outperformed the number of bugs (i.e. 40
bugs) that WITCHER framework was able to identified. Finally, we will propose a
Deep-Q Learning search heuristic algorithm over the PM-Aware search algorithm
in the state selection process to improve the searching strategy efficiently.
