Encyclopedia of Crash Dump Analysis Patterns, Volume 2, L-Z : Detecting Abnormal Software Structure and Behavior in Computer Memory, Third Edition

Dmitry Vostokov

ISBN 10: 1912636298 / ISBN 13: 9781912636297

Editore: Opentask, 2021

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nach der Bestellung gedruckt Neuware - Printed after ordering - This reference reprints with corrections, additional comments, and classification more than 370 alphabetically arranged and cross-referenced memory analysis patterns originally published in Memory Dump Analysis Anthology volumes 1 - 13. This pattern catalog is a part of pattern-oriented software diagnostics, forensics, prognostics, root cause analysis, and debugging developed by Software Diagnostics Institute. Most of the analysis patterns are illustrated with examples for WinDbg from Debugging Tools for Windows with a few examples from Mac OS X and Linux for GDB. The third edition includes more than 40 new analysis patterns, more than 30 new examples and comments for analysis patterns published in the previous editions, updated bibliography and links, improved illustrations, and debugger output snippets with extra visual highlighting. Volume 2 contains L-Z encyclopedia entries, bibliography, appendixes, and the index. Codice articolo 9781912636297

Dati bibliografici

Titolo: Encyclopedia of Crash Dump Analysis Patterns...

Casa editrice: Opentask

Data di pubblicazione: 2021

Legatura: Taschenbuch

Condizione: Neu

Edizione: terza edizione

Descrizione articolo

Riassunto:

This reference reprints with corrections, additional comments, and classification more than 370 alphabetically arranged and cross-referenced memory analysis patterns originally published in Memory Dump Analysis Anthology volumes 1 - 13. This pattern catalog is a part of pattern-oriented software diagnostics, forensics, prognostics, root cause analysis, and debugging developed by Software Diagnostics Institute. Most of the analysis patterns are illustrated with examples for WinDbg from Debugging Tools for Windows with a few examples from Mac OS X and Linux for GDB. The third edition includes more than 40 new analysis patterns, more than 30 new examples and comments for analysis patterns published in the previous editions, updated bibliography and links, improved illustrations, and debugger output snippets with extra visual highlighting. Volume 2 contains L-Z encyclopedia entries, bibliography, appendixes, and the index.

Informazioni sull?autore:

Dmitry Vostokov is an internationally recognized expert, speaker, educator, scientist, and author. He is the founder of pattern-oriented software diagnostics, forensics and prognostics discipline, and Software Diagnostics Institute. Vostokov has also authored more than 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 more than 25 years of experience in software architecture, design, development, and maintenance in a variety of industries including leadership, technical and people management roles. Dmitry also founded Syndromatix, Anolog.io, BriteTrace, DiaThings, Logtellect, OpenTask Iterative and Incremental Publishing, and Software Diagnostics Technology and Services (former Memory Dump Analysis Services) and Software Prognostics. In his spare time, he presents various topics on Debugging TV and explores Software Narratology, its further development as Narratology of Things and Diagnostics of Things (DoT), Software Pathology, and Quantum Software Diagnostics. His current areas of interest are theoretical software diagnostics and its mathematical and computer science foundations, application of formal logic, 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 cloud native computing, security, automation, functional programming, and applications of category theory to software development and big data.