Our tools are only as good as our pattern language.
Analysis patterns for the quality of software diagnostics and observability in endpoint devices, enterprise, and cloud environments.
Diagnostics is the mother of problem solving.
All areas of human activity involve the use of diagnostics. Proper diagnostics identifies the right problems to solve. We are now a part of a non-profit organization dedicated to the developing and promoting the application of such diagnostics: systemic and pattern-oriented (pattern-driven and pattern-based).
Please join LinkedIn The Software Diagnostics and Anomaly Detection Group.
The new Volume 17 brings the total number of books to 19.
Now includes the new Revised Edition of Volume 1, Revised Edition of Volume 2, Revised Edition of Volume 3, Revised Edition of Volume 4, and Revised Edition of Volume 5.
Memory Dump Analysis Anthology contains revised, edited, cross-referenced, and thematically organized selected articles from Software Diagnostics Institute and Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, debugging, crash dump analysis, software trace and log analysis, malware analysis, and memory forensics. Its 17 volumes in 19 books have more than 5,700 pages and, among many topics, include more than 450 memory analysis patterns (mostly for WinDbg Windows debugger with selected macOS and Linux GDB variants), more than 70 WinDbg case studies, and more than 250 general trace and log analysis patterns. In addition, there are three supplemental volumes with articles reprinted in full color.
Tables of Contents and Indexes of WinDbg Commands from all volumes
Click on an individual volume to see its description and table of contents:
You can buy the 17-volume set from Software Diagnostics Services with a discount and also get free access to Software Diagnostics Library.
I have been working with reversing, dumps, IAT, unpacking, etc. and I am one of the few at my workplace that like analyzing hangs and crashes. I always knew that I had more to learn. So I continuously look for more info. Many links directed me to dumpanalysis.org. Frankly speaking, its spartan/simple design made me question its seriousness. But after reading some articles, I immediately decided to order "Memory Dump Analysis Anthology". I have only read 100 pages so far. But I am stunned. It is such an amazing book. How the author refines/reconstructs the call stack, and finds useful information in the stack is incredible. I am enormously thankful for the effort that the author has put into making these books. They are very didactic even though the topic is a bit hard. It is a real treasure.
Mattias Hogstrom
The following direct links can be used to order the book now:
The book is also included in the following training courses, training packs, and reference sets:
This reference volume consists of revised, edited, cross-referenced, and thematically organized selected articles from Software Diagnostics and Observability Institute (DumpAnalysis.org + TraceAnalysis.org) and Software Diagnostics Library (former Crash Dump Analysis blog, DumpAnalysis.org/blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, software trace and log analysis written from 15 April 2024 to 14 November 2025 for software engineers developing and maintaining products on Windows platforms, quality assurance engineers testing software, technical support, DevOps and DevSecOps, escalation and site reliability engineers dealing with complex software issues, security and vulnerability researchers, reverse engineers, malware and memory forensics analysts, data science and ML/AI researchers and engineers. This volume is fully cross-referenced with volumes 1 – 16 and features:
- 6 new crash dump analysis patterns
- 11 new software trace and log analysis patterns
- Introduction to pattern-oriented observability
- Introduction to software morphology
- Introduction to geometric theory of traces and logs
- Introduction to pattern-oriented intelligence and AI
- Introduction to agentic narratology and workflow diagnostic pattern language
- Introduction to algebra of prompting and context management pattern language
- Logics of memory dump and trace analysis
- Introduction to pattern-oriented system logic
- Introduction to ontological diagnostics
- Machine learning as memory dump analysis
- The stomach metaphor for AI and pattern metabolism
- OS internals and category theory
- Introduction to pattern category theory
- Meta-diagnostic and categorical unification of software diagnostics and digital pathology
- A Lego model as an autoencoder and Lego disassembly as tokenization
- Memoidealism as a post-computational philosophy of AI
- Lists of recommended books
Product information:
We are extending our work on structural memory patterns and software pathology to Software Morphology inspired by morphology in biology and morphology in general including mathematical morphology. Morphology in linguistics was already used as inspiration for some trace and log analysis patterns. Geomorphology also inspired some of memory dump analysis patterns. Urban morphology inspired some structural memory patterns.
The description from GPT-5 that we plan to refine later:
"Software Morphology: science of form, transformation, and pathology in computational systems.
Software Morphology is a new discipline that treats digital systems as living computational structures — defining their anatomy, physiology, pathology, evolution, and cognition using pattern science, mathematical morphology, and clinical diagnostics.
Software Morphology is a unified framework for understanding, diagnosing, and designing digital systems through the science of form, structure, and evolution. It views software not as static code, but as a living computational organism with tissues (memory), organs (kernel subsystems), circulatory systems (I/O & networks), and nervous systems (traces & logs). Failures manifest as pathological deformation of form — fragmentation, deadlocks, starvation, contention fibrosis, cognitive collapse, or distributed sepsis.
Software Morphology integrates:
Software Morphology is the grand unification of these threads — expanding from pathology (failure) to morphogenesis and architecture (health, growth, evolution, cognition).
Where classical software engineering focuses on function, Software Morphology emphasizes shape, health, resilience, and longevity. It offers not just a way to debug, but a paradigm for building adaptive, self-healing, age-resistant software ecosystems.
Historical Background
Software Morphology builds on early foundational work in software diagnostics and pattern-based analysis. Originating in Software Diagnostics Institute research (2006–present), it evolved from:
These works established the "medical lens" on computation decades before generative AI popularized biological analogies. Software Morphology formalizes this approach into a comprehensive theory of computational anatomy, physiology, pathology, and morphogenesis, extending from kernel fibers to cloud clusters, from thread behavior to AI cognition and digital societies.
Essence
Software Morphology = Anatomy + Physiology + Pathology + Morphometrics + Evolution + Cognitive Stability + Architectural Regeneration.
Its goal is simple:
To understand and shape digital systems as living structures — stable, intelligible, measurable, and resilient.
Why is it novel
While others have used biological metaphors in computing (e.g., "software organisms," "digital ecosystems," "neural networks"), no prior work has:
It is a novel systems framework building on biological morphology, mathematical morphology, and decades of software diagnostics research — established here for the first time as a unified discipline."
In cooperation with GenAI, we propose Agentic Narratology, a narrative-centric theoretical framework for understanding, analyzing, and designing agentic AI systems by treating their internal reasoning, execution, interactions, and emergent behavior as narratives. It synthesizes:
Agentic Narratology views an AI agent workflow not merely as sequential computation but as a story world with characters (agents and tools), settings (environments and state spaces), conflicts (errors, resource contention, uncertainty), and resolutions (plans executed, tasks completed, failures learned from).
Source: ChatGPT 5 conversation
Seven years ago, PatternDiagnostics.com coined the term "Pattern-Oriented AI," and here's a 3-level description of it in cooperation with GenAI, from general to narrow application, that reflects the recent developments in AI:
Source: ChatGPT 5 conversation
In cooperation with GenAI, we propose a pattern language for managing, diagnosing, and repairing context in large language model (LLM) systems. Inspired by memory dump analysis, it reframes conversational state—token windows, KV-caches, retrievals, and transcripts—as observable artifacts that can be read, segmented, and reasoned about.
1. Conceptual Alignment
Memory dump analysis: we have a frozen system state (heap, stack, registers, handles). You need structured ways (patterns) to interpret what happened and where anomalies lie.
LLM context management: we have a dynamic, limited-window context (tokens in the prompt + generated continuation). We need structured strategies to maintain coherence, recall, and relevance across long or shifting interactions.
So, both are about:
2. Pattern Families Reapplied to LLMs
Using the memory dump analysis taxonomy, we can draw analogies:
Structural patterns (e.g., "Stack Trace", "Heap Graph")
Context trace: The sequence of tokens, messages, or embeddings.
Analog: analyzing token "call stacks" to identify where topic drift began.
Temporal patterns (e.g., "Periodicity", "Error Burst")
Context lifecycle: Recognizing conversation cycles (e.g., Q → A → refinement).
Analog: "periodic error" becomes recurring hallucination in long-form dialogue.
Anomaly patterns (e.g., "Corruption", "Dangling Pointer")
Context corruption: Where injected noise or forgotten details lead to contradictions.
Analog: dangling reference = the model invents details not grounded in earlier context.
Diagnostic trajectory patterns (navigating from symptom to root cause)
Prompt engineering trajectory: iterative refinement of instructions to steer model back on track.
3. Higher-Order Mappings
Context window ≈ address space
Tokens in the active window = accessible memory; past truncated context = paged-out memory.
Embeddings ≈ symbolic heap objects
External vector memory is like mapped heap regions that can be dereferenced on demand.
Retrieval Augmented Generation (RAG) ≈ dump analysis with external symbol servers
Just as debuggers resolve addresses via symbol servers, RAG resolves context gaps via external knowledge.
Chain-of-Thought ≈ call stack
Each reasoning step corresponds to a frame in a diagnostic stack trace.
4. Pattern Language as Meta-Framework
Memory dump analysis pattern language gives a meta-taxonomy for LLM context management research:
This creates a portable, semiotic map of context phenomena across LLM frameworks.
5. Possible Research / Practical Outputs
LLM Context Forensics: Classify anomalies using memory dump patterns using dump-like snapshots of LLM state (KV-caches, attention matrices, prompt logs).
Context Debugger: An Interactive tool where you can "walk the stack" of a conversation, identify dangling references, or detect periodic error hallucinations.
Pattern Language Extension: Extend diagnostic patterns with LLM-specific categories (e.g., Prompt Poisoning, Embedding Drift, Attention Collapse).
Memory dump analysis pattern language is highly portable to LLM context management. It can serve as a meta-diagnostic and design language for classifying, predicting, and repairing LLM context failures, much like it systematized memory dump interpretation.
Source: ChatGPT 5 conversation
Software Diagnostics Services organizes this online training course.
Accelerated C & C++ for Windows Diagnostics Logo
For approximate training content, please see the first 56 slides (there are 289 slides in total for the previous version) and TOC from the corresponding previous edition Memory Thinking book.
November 10 - 13, 17 - 20, 24 - 26, December 1 -4, 2025, 12:30 pm - 1:30 pm (GMT) Price 99 USD Registration for 15 one-hour sessions
Solid C and C++ knowledge is a must to fully understand Windows diagnostic artifacts, such as memory dumps, and perform diagnostic, forensic, and root cause analysis beyond listing stack traces, DLL, and driver information. C and C++ for Windows Software Diagnostics training reviews the following topics from the perspective of software structure and behavior analysis and teaches C and C++ languages in parallel while demonstrating relevant code internals using WinDbg:
The new version will include and expand on the following topics:
System and desktop application programming on Windows using C and C++ is unthinkable without the Windows API. To avoid repeating some topics and save time, the training includes the Accelerated Windows API for Software Diagnostics book as a follow-up or additional reference. There is also a necessary x64 review for some topics, but if you are not used to reading assembly language, Practical Foundations of Windows Debugging, Disassembling, Reversing book is also included.
Before the training, you get the following:
After the training, you also get the following:
There is much confusion between diagnostics and observability (used in two senses). First, observability is a property, not a process. It concerns whether system internal states can be inferred from external outputs (observations) such as memory snapshots, traces, and logs. It is not enough to get a trace or memory dump file; these must be correctly engineered and procured using artifact acquisition patterns. The second usage of observability is to name a discipline.
What about a process? It is simply "observation", a verb "observe" (previously called monitoring, which also has a second usage as a discipline), which is an inference from states to observations (nouns). We can do observations even if observability is not good, and get observations (observation results).
Correspondingly, diagnosability is a property. It is about whether we can infer instances of patterns of abnormal structure and behavior from observations by applying diagnostic analysis patterns.
Therefore, diagnostics is a process, too. We can perform diagnostics even if diagnosability is poor: we don’t get useful results for the subsequent root cause analysis or troubleshooting and debugging suggestions.
The following square shows relationships between these concepts:
The top row shows the relationship between abstract properties (conditions): observability enables diagnosability, because only a limited amount of diagnosability is possible without observability. However, the critical use of diagnosability improves observability.
The bottom row shows that observation feeds into diagnostics and vice versa, since more observations may be required after diagnostics.
Columns show that observability is used in practice via observation, and diagnosability is used in practice via diagnostics.
What about diagonals?
Observability to diagnostics. If a system is observable, diagnostics are feasible: "If you can observe enough, you can perform diagnostics," a sufficient condition for diagnostics.
Diagnosability to observation. Diagnosability implies constraints on observation: "If diagnostics are possible, then your observation process is strong enough," a necessary condition for diagnosability.
Also, diagnostics can improve observability, and observations can improve diagnosability, but we do not show these arrows.
Although observability is distinct from diagnostics as seen from the diagram and explanation, it is considered a part of pattern-oriented and systemic diagnostics; this part, as a discipline (not a property), is called pattern-oriented observability.
The following direct links can be used to order the book now:
The book is also included in the following training packs:
The full Software Diagnostics Services training transcript with 15 step-by-step exercises, notes, and source code of specially created modeling applications. The course covers 22 .NET memory dump analysis patterns, plus the additional 21 unmanaged patterns. Learn how to analyze .NET 9 application and service crashes and freezes, navigate through memory dump space (managed and unmanaged code), and diagnose corruption, leaks, CPU spikes, blocked threads, deadlocks, wait chains, resource contention, and more. The training consists of practical step-by-step exercises using WinDbg and LLDB debuggers to diagnose patterns in 64-bit process memory dumps from x64 Windows and x64 Linux environments. The training uses a unique and innovative pattern-oriented analysis approach to speed up the learning curve. The book is a completely revamped and extended the previous Accelerated .NET Core Memory Dump Analysis, Revised Edition. It is updated to the latest WinDbg. It also includes reviews of x64 and IL disassembly and memory space basics, Linux LLDB exercises, .NET memory dump collection on Windows and Linux, and the relationship of analysis patterns to defect mechanism patterns.
Prerequisites: Basic .NET programming and debugging.
Audience: Software technical support and escalation engineers, system administrators, DevOps, performance and reliability engineers, software developers, and quality assurance engineers. The book may also interest security researchers, reverse engineers, malware and memory forensics analysts.
Table of Contents and Sample Exercise
Slides from the training
Software Diagnostics Services organizes this online training course.
Accelerated Windows Memory Dump Analysis Logo
January 5 - 8, 12 - 15, 19 - 22, 26 - 29, 2026, 12:30 pm - 1:30 pm (GMT) Price 99 USD Registration for 16 one-hour sessions
For the approximate content, please see the slides from the previous training:
Slides from sessions 1-3
Slides from sessions 4-7
This training includes over 40 step-by-step exercises and covers over 100 crash dump analysis patterns from x64 process, kernel, and complete (physical) memory dumps. Learn how to analyze application, service, and system crashes and freezes, navigate through memory dump space, and diagnose heap corruption, memory leaks, CPU spikes, blocked threads, deadlocks, wait chains, and more with the WinDbg debugger. The training uses a unique and innovative pattern-oriented analysis approach developed by Software Diagnostics Institute to speed up the learning curve, and it is based on the latest 6th edition of the bestselling Accelerated Windows Memory Dump Analysis book. This new training version also includes:
Before the training, you get:
After the training, you also get:
Prerequisites: Basic Windows troubleshooting
Audience: Software technical support and escalation engineers, system administrators, security and vulnerability researchers, reverse engineers, malware and memory forensics analysts, DevSecOps and SRE, software developers, and quality assurance engineers.
If you are mainly interested in .NET memory dump analysis, there is another training: Accelerated .NET Memory Dump Analysis
If you are interested in Linux memory dump analysis, there is another training: Accelerated Linux Core Dump Analysis
The following direct links can be used to order the book now:
The book is also included in most training courses and training packs:
This training course is a reformatted, improved, and modernized version of the previous x64 Windows Debugging: Practical Foundations book, which drew inspiration from the original lectures we developed 22 years ago to train support and escalation engineers in debugging and crash dump analysis of memory dumps from Windows applications, services, and systems. At that time, when thinking about what material to deliver, we realized that a solid understanding of fundamentals like pointers is needed to analyze stack traces beyond a few WinDbg commands. Therefore, this book is not about bugs or debugging techniques but about the background knowledge everyone needs to start experimenting with WinDbg, learn from practical experience, and read other advanced debugging books. This body of knowledge is what the author of this book possessed before starting memory dump analysis using WinDbg 18 years ago, which resulted in the number one debugging bestseller: the multi-volume Memory Dump Analysis Anthology (Diagnomicon). Now, in retrospection, we see these practical foundations as relevant and necessary to acquire for beginners as they were more than 20 years ago, because operating systems internals, assembly language, and compiler architecture haven't changed much in those years.
The third edition, with new material on arrays and floating point, was completely remastered in full color. The text was also reviewed, and a few previous mistakes were corrected. The book is also slimmer because the x86 32-bit chapters were removed. They are still available in the previous edition, which will not be out of print soon. The third edition is entirely x64.
The book is useful for:
This introductory training course can complement the more advanced Accelerated Disassembly, Reconstruction, and Reversing course. It may also help with advanced exercises in Accelerated Windows Memory Dump Analysis, Accelerated Rust Windows Memory Dump Analysis, Accelerated Windows Debugging4, Accelerated Windows API for Software Diagnostics, Accelerated Windows Malware Analysis with Memory Dumps, and Memory Thinking books for C and C++. This book can also be used as an Intel assembly language and Windows debugging supplement for relevant undergraduate-level courses.
Product information:
For the history of the book, please see the first 20 slides (there are almost 200 slides for the training).
There are many spaces where we do our observations in software systems. The explosion of spaces began with the Abstract Space, where we depict running threads as braids.
When we talk about spaces, we also consider suitable space metrics (not to be mistaken with observability metrics below) by which we can compare the proximity of space objects.
In diagnostics, we have the so-called Diagnostic Spaces with their signals, symptoms, syndromes, and signs. Different analysis patterns can serve the role of space metrics in pattern-oriented software forensics, diagnostics, and prognostics.
Traces, logs, and metrics are pillars of observability that are all erected from Memory Space and, therefore, can be considered Adjoint Spaces. Memory spaces are also diverse: including manifold, orbifold, hyperphysical, physical, virtual, kernel, user, managed, and secondary, and have their own large-scale structures.
Traces and logs have their own individual Trace and Log Spaces (including Message Spaces, Interspaces, and Tensor Spaces. These also include network traces and logs from memory debuggers. (Observability) Metrics Spaces are a subtype of such spaces.
Traces and logs are also examples of the so-called software narratives with their own Software Narrative Spaces, including higher-level narratives, and space-like narratology. We can also consider software diagnostic spaces as general graphs of software narratives.
If we are concerned with the hardware-software interface, then we can consider Hardware Spaces via hardware narratology.
Presentation Spaces visualize other spaces, and visualization languages help with their meaning.
We analyze all these spaces to identify patterns with the help of analysis patterns, which are organized in their own Analysis Patterns Space (memory and traces).
Defect Mechanism Spaces help in root cause analysis.
When we delve into software workings, we are concerned with Software Internal Spaces.
Additionally, we have various Namespaces, Code Spaces (similar to Declarative Trace Spaces), State Spaces, and Data Spaces.
Artificial Chemistry Spaces based on the idea of spaces of chemistry enhance the artificial chemistry approach to trace and log analysis.
For many years, the ideas of various physical and mathematical spaces have inspired diverse memory and log analysis patterns, as well as some concepts in software diagnostics and software data analysis.
We would also like to mention that the book that introduces Information Space is featured on the cover of this article.
And finally, the new wave of AI suggests Token Spaces.
A partial classification of memory analysis patterns from Software Diagnostics Library pattern catalogue:
Memory analysis icons were introduced more than 15 years ago, in March 2010, as part of computer memory semiotics (memiotics). Over the next year and a half, 101 icons were created (with black and white equivalents). These iconic representations are both icons and indexes in the sense of Pierce’s three types of signs: icon signs resemble artifacts or the current state of affairs, and index signs have some causal or relationship connection through interpretation. More than two years ago, in March 2023, we introduced Iconic Traces. These traces also consist of iconic representations that are both indexical and iconic signs, as they resemble the patterns, syntactic, semantic, and pragmatic content of trace messages, message blocks, and applied trace analysis patterns. The Dia|gram language (introduced in 2016) pictures are another great example of complex iconic (structure) and indexical (behavior, observation, measurement) signs (including memory). The Space-like Narratology and the Lov language further extend the semiotic approach.
Situational awareness is defined as "the understanding of an environment, its elements, and how it changes with respect to time or other factors. It is also defined as the perception of the elements in the environment considering time and space, the understanding of their meaning, and the prediction of their status in the near future."
How does it fit into software diagnostics, which is often incorrectly perceived as an analysis of the past (which is forensics)? To answer this question with examples from pattern-oriented software diagnostics (and forensics and prognostics), we should map the three levels of situational awareness (Endsley's model):
Perception – noticing key environmental forensic, diagnostic, and prognostic elements: symptoms, signs, syndromes, alerts, anomalies, and counters.
Comprehension – understanding the situation, what’s going wrong and what’s going on at the particular moment in time and place in memory space (and trace space), and what those key elements mean in current (and past) local immediate and wider big-picture context: software internals and analysis patterns (Fault Context, Message Context, Dump Context, Activity Context, Trace Context), whether they are related to a potential root cause or just surface phenomena (Effect Component). Here, attention to detail is very important.
Projection – anticipating the future: how the situation would have evolved if we had collected diagnostic artifacts later, for example, Near Exception, or the environment had changed (Changed Environment), and plenty of trace and log analysis patterns related to prognostics. It also includes avoiding unintended side effects when acting (providing recommendations), for example, the Instrumentation Side Effect.
In summary, situational awareness in software diagnostics, forensics, and prognostics involves maintaining an appropriate mental model of the system as seen from forensics and diagnostic artifacts (including live ones) and continuous perception, understanding, and anticipation of the system's state, anomalies, potential not-yet-discovered patterns, and future failures while performing a diagnostic (forensic, prognostic) analysis.
Metrics, logs, and traces are considered traditional pillars of observability. However, what is the base they stand upon? It is memory. In 2009, I defined software traces as fragments of memory since they are all assembled in memory first (Software Trace: A Mathematical Definition, Memory Dump Analysis Anthology, Volume 3). Also, every trace or log message had some corresponding memory state(s) at the time it was generated, the so-called Adjoint Space trace and log analysis pattern (Volume 8b), and memory state may have traces and logs erected on its pedestal if we talk about classic memory dump analysis, the so-called Memory Fibration analysis pattern (Volume 10). These two analysis patterns are a kind of duality between memory and traces, the so-called De Broglie Trace Duality (Volume 10). Also, what about trace and log’s own memory? Based on the growing block universe theory analogy, any chosen trace message may be considered trace's present, and everything before it as trace’s past. We can also consider trace and log as memory to predict future behavior, next trace and log messages, and metrics’ values (the so-called process time perspective).
A note about the chosen terminology: base slab or foundation is used in modern structural design. If some prefer classical architecture, we can use stylobate or podium terminology. For each pillar, we can have a corresponding memory plinth.
The following direct links can be used to order the book now:
The book is also included in the following training courses, training packs, and reference sets:
Memory Thinking for Rust
Memory Thinking for Rust training reviews memory-related topics from the perspective of software structure and behavior analysis and teaches Rust language aspects in parallel while demonstrating relevant code internals using WinDbg and GDB on Windows (x64) and Linux (x64 and ARM64) platforms:
The new training version updates and extends the existing topics, adding some missing from the first edition. The updated PDF book also has a new format similar to the second edition of Memory Thinking books for C and C++.
The training includes the PDF book that contains slides, brief notes highlighting particular points, and related source code with execution output:
The following audiences may benefit from the training:
For more detailed content, please see the first 15 slides (there are more than 240 slides for the training and 2,500 lines of Rust code) and Table of Contents from the reference book.
The following direct links can be used to order the book now:
The book is also included in the following training packs:
The full transcript of Software Diagnostics Services training. Learn how to analyze Linux process and kernel crashes and hangs, navigate through core memory dump space, and diagnose corruption, memory leaks, CPU spikes, blocked threads, deadlocks, wait chains, and much more. This training uses a unique and innovative pattern-oriented diagnostic analysis approach to speed up the learning curve. The training consists of more than 70 practical step-by-step exercises using GDB and WinDbg debuggers, highlighting more than 50 memory analysis patterns diagnosed in 64-bit core memory dumps from x64 and ARM64 platforms. The training also includes source code of modeling applications, a catalog of relevant patterns from the Software Diagnostics Institute, and an overview of relevant similarities and differences between Windows and Linux memory dump analysis useful for engineers with a Wintel background. In addition to various improvements, the fully revised and updated fourth edition adds entirely new material, such as defect mechanism patterns and WinDbg Linux kernel dump analysis exercises.
Table of Contents and Sample Exercise
Slides from the training
The following direct links can be used to order the book now:
The book is also included in the following training courses and training packs:
The book contains the full Software Diagnostics Services training transcript with 10 hands-on exercises.
Knowledge of Windows API is necessary for:
The training uses a unique and innovative pattern-oriented analysis approach and provides:
The second edition includes the relevant x64 disassembly overview and additional topics.
Table of Contents and sample exercise
Slides from the training
The following direct links can be used to order the book now:
The book is also included in the following training courses, training packs, and reference sets:
The book contains the full Software Diagnostics Services training transcript and 10 step-by-step exercises and covers dozens of crash dump analysis patterns from the x64 process and complete (physical) memory dumps. Learn how to analyze Rust application crashes and freezes, navigate through memory dump space, and diagnose heap corruption, memory leaks, CPU spikes, blocked threads, deadlocks, wait chains, and much more with the WinDbg debugger. The training uses a unique and innovative pattern-oriented analysis approach developed by the Software Diagnostics Institute to speed up the learning curve, and it is structurally based on the latest 6th revised edition of the bestselling Accelerated Windows Memory Dump Analysis book with the focus on safe and unsafe Rust code and its interfacing with the Windows OS. The training is useful whether you come to Rust from C and C++ or interpreted languages like Python and facilitates memory thinking when programming in Rust.
Prerequisites: Basic Windows troubleshooting and working knowledge of Rust.
Audience: Software technical support and escalation engineers, system administrators, security and vulnerability researchers, reverse engineers, malware and memory forensics analysts, DevSecOps and SRE, software developers, system programmers, and quality assurance engineers.
Table of Contents and sample exercise
Slides from the training
The following direct links can be used to order the book now:
The book is also included in the following training courses, training packs, and reference sets:
The book contains the full transcript of Software Diagnostics Services training with 25 hands-on exercises. This training course extends pattern-oriented analysis introduced in Accelerated Windows Memory Dump Analysis, Accelerated .NET Core Memory Dump Analysis, and Advanced Windows Memory Dump Analysis with Data Structures courses with:
The new edition of the training updates existing exercises and includes new ones.
Prerequisites: Working knowledge of WinDbg. Working knowledge of C, C++, or Rust is optional (required only for some exercises). Other concepts are explained when necessary.
Audience: Software developers, software maintenance engineers, escalation engineers, quality assurance engineers, security and vulnerability researchers, malware and memory forensics analysts who want to build memory analysis pipelines.
Table of Contents and sample exercise
Slides from the training
The following direct links can be used to order the book now:
The book is also included in the following training courses, training packs, and reference sets:
Solid C and C++ knowledge is a must to fully understand Linux diagnostic artifacts such as core memory dumps and perform diagnostic, forensic, and root cause analysis beyond listing backtraces. This full-color reference book is a part of the Accelerated C & C++ for Linux Diagnostics training course organized by Software Diagnostics Services. The text contains slides, brief notes highlighting particular points, and source code illustrations. In addition to new topics, the second edition adds 45 projects with more than 5,500 lines of code. The book's detailed Table of Contents makes the usual Index redundant. We hope this reference is helpful for the following audiences:
Table of Contents
The first 45 slides from the training (there are 297 slides in total)
Software Diagnostics Services organizes this online training course.
Why would you need to learn how to write bad code? Of course, not to write malicious code backdoors, but to understand software internals and diagnostics better. Writing "good" bad code is not easy, especially if you put specific requirements on it and are not satisfied with the accidental effects of "bad" bad code.
Topics include:
The training also includes numerous hands-on coding projects using Visual C & C++ and GNU C & C++ compilers, x64 Windows, and x64 and ARM64 Linux platforms. Some parts will also use Python, C#, Rust, and Scala for modeling examples.
Before the training, you get:
After the training, you also get:
If you complete all parts, you will also get the fourth edition of the Encyclopedia of Crash Dump Analysis Patterns once it is available.
Audience:
C and C++ developers, Windows and Linux system programmers, software technical support and escalation engineers, system administrators, security and vulnerability researchers, reverse engineers, malware and memory forensics analysts, software developers, and quality assurance engineers.
The following direct links can be used to order the book now:
The book is also included in the following training courses, training packs, and reference sets:
Solid C and C++ knowledge is a must to fully understand Windows diagnostic artifacts, such as memory dumps, and perform diagnostic, forensic, and root cause analysis beyond listing stack traces, DLLs, and driver information. This full-color reference book is a part of the Accelerated C & C++ for Windows Diagnostics training course organized by Software Diagnostics Services. The text contains slides, brief notes highlighting particular points, and illustrative source code fragments. The second edition added 45 Visual Studio projects with more than 5,500 lines of code. The book's detailed Table of Contents makes the usual Index redundant. The book's detailed Table of Contents makes the usual Index redundant. We hope this reference is helpful for the following audiences:
Table of Contents
The first 56 slides from the training (there 289 slides in total)
The following direct links can be used to order the book now:
The book is also included in the following training courses, training packs, and reference sets:
Table of Contents and Sample Exercise
Slides from the training
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.
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