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Learn how to enhance defect triaging in storage systems through AI integration and knowledge graphs in this 51-minute conference talk. Discover the unique challenges of bug detection in complex storage systems that distinguish them from general-purpose software, including their tight integration with operating system kernels, device drivers, and hardware devices. Explore why conventional AI-based bug-tracking solutions, typically trained on general codebases, fail to deliver effective results in storage environments where hundreds of threads and processes write to shared log files without transactional guarantees. Understand a novel approach that supplements system code with knowledge extracted from high-level integration test cases written in human-readable scripting languages like Python, which capture end-to-end system behavior more effectively than unit tests. Examine how converting insights from integration tests into structured knowledge graphs provides AI bug-triaging agents with rich contextual understanding of system interactions, inter-process communications, and hardware events. Gain insight into how this targeted fusion of code analysis and integration-test-based knowledge significantly enhances both speed and accuracy of bug identification in storage software, empowering agents to pinpoint issues hidden in the complex interactions between kernel-mode calls, user-mode processes, and low-level device drivers. Evaluate early findings that suggest this methodology could transform how complex system bugs are tracked and resolved in storage solutions.
