Synopsis
The full transcript of Software Diagnostics Services training with more than 20 step-by-step exercises using WSL and Hyper-V environments, notes, and source code of specially created modeling applications in C, C++, and Rust. Learn live local and remote debugging techniques in the kernel, user process, and managed spaces using WinDbg, GDB, LLDB, rr, and KDB, KGDB debuggers. The unique and innovative course teaches unified debugging patterns applied to real problems from complex software environments. A necessary x64 and ARM64 review is also included.
Prerequisites: Working knowledge of one of these languages: C, C++, Rust. Operating system internals and assembly language concepts are explained when necessary.
Audience: Software engineers, software maintenance engineers, escalation engineers, SRE, DevOps and DevSecOps, cloud engineers, security and vulnerability researchers, malware and memory forensics analysts who want to learn live memory inspection techniques.
About the Author
Dmitry Vostokov is an internationally recognized expert, speaker, educator, scientist, inventor, and author. He founded the pattern-oriented software diagnostics, forensics, and prognostics discipline (Systematic Software Diagnostics) and Software Diagnostics and Observability Institute. Vostokov has also authored over 50 books on software diagnostics, anomaly detection and analysis, software and memory forensics, root cause analysis and problem solving, memory dump analysis, debugging, software trace and log analysis, reverse engineering, and malware analysis. He has over 30 years of experience in software architecture, design, development, and maintenance in various industries, including leadership, technical, and people management roles. Dmitry founded OpenTask Iterative and Incremental Publishing and Software Diagnostics Technology and Services (former Memory Dump Analysis Services). In his spare time, he explores Software Narratology and Quantum Software Diagnostics. His interest areas are theoretical software diagnostics and its mathematical and computer science foundations, application of formal logic, semiotics, artificial intelligence, machine learning, and data mining to diagnostics and anomaly detection, software diagnostics engineering and diagnostics-driven development, diagnostics workflow and interaction. Recent interest areas also include functional programming, cloud native computing, monitoring, observability, visualization, security, automation, applications of category theory to software diagnostics, development and big data, and diagnostics of artificial intelligence.
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