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.

翻译：多年来，由于非易失性持久内存（PM）在文件或存储系统上的不一致行为，检测PM崩溃一致性的错误以及开发新的工具来识别这些错误面临着诸多挑战。在本文中，我们评估了一个开源自动错误检测工具（即AGAMOTTO），用于测试NVM级哈希持久内存（PM）应用程序，以识别持久（主）内存中的性能和正确性PM错误。此外，我们的可靠验证工具能够在PMDK库中发现65个新的NVM级哈希错误，这一数量超过了WITCHER框架所能识别的错误数量（即40个错误）。最后，我们将提出一种深度Q学习搜索启发式算法，替代状态选择过程中的PM感知搜索算法，以高效改进搜索策略。