Software Diagnostics Library

The Trace Multiphysics analysis pattern applies several Trace Fields to the same sequence of trace messages and studies both their individual strengths and their interactions. A Trace Field associates every message in a trace domain M with a value in some analytical range T. Although the field is defined over M, its values need not be extracted solely from that trace. A value may be derived from the message itself, inferred from its surrounding context, or assigned using correlated evidence from other traces, logs, metrics, memory dumps, snapshots, models, or external knowledge sources. The value does not need to be numerical, although numerical or ordered values are useful when field strength must be visualized. For example, one field may represent execution significance, another resource pressure, and a third semantic or user-visible importance:

However, Trace Multiphysics is not merely the display of several independent Trace Fields: its essential feature is their coupling. A change in one field may affect another field. For example, increased resource pressure may slow execution. Slower execution may trigger retries. Repeated retries may further increase resource pressure. Eventually, the combined effect may change the semantic or user-visible outcome. The coupling diagram represents these relationships, and arrow thickness indicates coupling strength:

A simple conceptual form is: C_ij​(m)=g(f_i​(m), f_j​(m), R_ij​(m)), where f_i​: M → T_i and f_j​: M → T_j, f_i(m) and f_j(m) are the strengths or values of fields i and j at message m, C_ij(m) is the coupling between fields i and j at message m, and R_ij(m) represents evidence of an interaction between the two fields, obtained from the trace itself or from correlated diagnostic sources. This formula defines coupling at message m. More generally, a coupling may relate field values at different messages when the influence is delayed or propagated through the trace: C_ij​(m_a, m_b)=g(f_i​(m_a), f_j​(m_b), R_ij​(m_a, m_b)), where m_a ⪯ m_b. Here, R_ij​(m_a, m_b) represents evidence relating the value f_i(m_a) of field i to the value f_j(m_b) of field j, whether that evidence is contained in the primary trace or supplied through correlated artifacts. High values in two fields do not automatically establish strong coupling. The available diagnostic evidence must also support the conclusion that one field influences, constrains, amplifies, transforms, or explains the other. Such evidence may occur in the primary trace or in correlated traces, logs, metrics, snapshots, models, or other diagnostic artifacts:

The mapping f_i is defined over the trace domain M, but its construction may depend on evidence outside M. A more explicit form is: f_i(m) = h_i(m, E_i(m)), where E_i(m) is the evidence associated with message m. This evidence may come from the message itself, nearby messages, other traces or logs, metrics, snapshots, memory dumps, learned models, or other correlated diagnostic sources. Correlation may be established through timestamps, identifiers, causal links, shared entities, execution context, or other relations.

When no ordinary field value can be assigned, T_i may include special values representing unknown, unavailable, or not applicable, preserving the definition of the field over the complete trace domain.

In computational modeling, multiphysics simulation studies several aspects of a physical system and their interactions simultaneously. A model may combine thermal, structural, fluid, electromagnetic, or other processes, together with the coupling and boundary conditions between them. Trace Multiphysics transfers this structural principle to software diagnostics. The analogy does not imply that all software behavior should be described by physical equations. Instead, it provides a disciplined way to analyze several interacting dimensions without reducing the incident to one isolated perspective.

The connection is especially appropriate in software diagnostics because software can be its own model. Software execution states and execution artifacts can be copied, preserved, replayed, or analyzed independently. Traces, logs, metrics, memory dumps, and snapshots are symbolic and digital artifacts produced by the software system itself. In this diagnostic sense, software performs a form of self-simulation: its execution generates artifacts that model selected aspects of its own structure and behavior. A trace is not a complete reproduction of the running system. It is a selective, instrumented self-model. Different Trace Fields map messages in that self-model to values in different analytical ranges, each representing a distinct analytical dimension. Some values may be extracted directly from the trace, while others may be inferred, enriched, or projected from correlated traces, logs, metrics, memory dumps, snapshots, models, or external knowledge. Trace Multiphysics studies those fields together and examines how they interact. Thus, multiphysics simulation models several interacting physical processes, whereas Trace Multiphysics analyzes several interacting fields defined over a software-generated model of software behavior and potentially enriched from other diagnostic sources.

Trace Field provides the foundational assignment or mapping from trace messages to values in one analytical range. The field is defined over a selected trace domain, but the evidence used to assign its values may originate inside or outside that domain. Trace Multiphysics extends this principle by applying several Trace Fields to the same trace domain and analyzing their simultaneous values, changing strengths, overlap at individual messages, dependencies, feedback loops, coupling strengths, and combined explanatory effects. The relationship can be summarized as follows: Trace Field maps trace messages to values in one analytical range, while Trace Multiphysics studies several coupled Trace Fields defined over the same trace domain, with their values potentially derived from or enriched by evidence from multiple diagnostic sources.

Trace Multiphysics also connects to several recent analysis patterns. In Trace Reactance, inductive and capacitive effects can be understood as specific forms of inter-field coupling and temporal distortion produced by interacting state fields. Karnaugh Map is also multidimensional but primarily Boolean and combinatorial, whereas Trace Multiphysics accommodates continuous or graded fields, propagation, and feedback loops. Trace World may provide a broader shared diagnostic context in which coupled Trace Fields are interpreted. Bethe Ansatz is a constrained form of Trace Multiphysics in which global behavior is reconstructed from composable pairwise interaction motives.

The pattern is particularly relevant to AI/ML and agentic AI systems. Multiphysics approaches in machine learning usually apply ML to coupled physical processes or incorporate multiple physical priors into learning. Multiphysics-Inspired AI/ML Observability and Diagnostics, by contrast, treats the AI/ML system itself as a collection of interacting analytical fields, including data, optimization, information, computation, uncertainty, control, and semantic outcome fields, defined over a common trace domain constructed from correlated training, inference, and operational events. Its observability aspect concerns the instrumentation, correlation, and enrichment required to construct these fields from training logs, model evaluations, infrastructure metrics, distributed traces, tool-call records, user interactions, safety assessments, and other evidence sources. Its diagnostic aspect concerns how fields interact through their values and couplings, how disturbances propagate across fields, how feedback loops emerge among them, and how their combined effects produce system-level behavior.

In an agentic AI system, for example, increased tool latency may cause retries. Retries may enlarge the accumulated context and increase computational pressure. Context growth may change the relative prominence of relevant information and increase uncertainty, affecting planning, tool selection, and the final semantic outcome. A degraded outcome may provoke further retries, creating a reinforcing feedback loop. Observability provides the correlated evidence needed to construct, expose, and track these fields, while diagnostics analyzes their values and couplings to explain the resulting agent behavior. The behavior cannot be explained adequately by any one field in isolation.

In summary, Trace Multiphysics is a trace and log analysis pattern that examines several Trace Fields defined over the same trace domain, whether their values are derived from that trace alone or assigned using correlated evidence from other diagnostic sources. It studies the immediate, delayed, reinforcing, constraining, transforming, and explanatory couplings through which their combined behavior emerges. Multiphysics-Inspired AI/ML Observability and Diagnostics extends this principle to AI/ML and agentic systems: observability constructs and exposes the relevant fields from distributed evidence, while diagnostics analyzes their interactions and explains the emergent system behavior.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Usually, software traces and logs are sorted by time.

Spatial Form is a specialized Sorted Trace in which trace or log messages are sorted by spatial, topological, or diagnostic proximity to a chosen origin component, device, process, service, or subsystem. Instead of reading the trace solely as a chronological sequence, we choose a diagnostic origin and arrange messages by their distance from that origin. Within each distance layer, the original local time order may still be preserved.

The pattern name comes from Joseph Frank’s The Idea of Spatial Form that is associated with reading narrative structure through juxtaposition and relational arrangement rather than only through linear chronological progression; the concept was introduced into literary discussion through his 1945 essay and later collected with reconsiderations in his book.

Sorted Trace is the more general pattern: messages are sorted according to some attribute value, for example, by TID, ATID, message type, message invariants, or message data.

For Spatial Trace, the distance may come from network topology, service dependency graph, component containment, process/thread ownership, device hierarchy, pipeline stage distance, proxy/gateway chain, address-space relation, storage or shard topology, causal adjacency, and many others. The resulting trace is not anti-temporal. It is spatially primary and temporally secondary.

Spatial Trace analysis pattern may help answer these questions: What happened around this proxy? Which nearby component first showed abnormal behavior? How did the request propagate outward? Was the fault local, adjacent, or remote? It may help distinguish local symptoms, adjacent symptoms, downstream effects, remote dependencies, and external causes. It gives the trace a layered diagnostic structure and spatial Layered Periodization. The pattern is therefore both a sorting technique and a reading strategy.

This pattern is especially useful for distributed systems, microservices, network devices, storage stacks, cloud control planes, request pipelines, proxies, gateways, and agentic AI workflows where activity is spread across many components.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Some Dia|gram language illustrations of trace and log analysis patterns can be easily converted to more usual graphs. For example, Trace Field can be converted to Combed Trace or Adjoint Threads of Activity:

The resulted series can be plotted as a graph:

If time is values are not uniform Time Scale analysis pattern may be involved with the constructed Motivic Trace. We call this analysis pattern Trace Graph.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

If we take Combed Trace for Thread of Activity or some Adjoint Thread of Activity, strip other message content, and then trace all non-empty values we get Trace Contour:

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

A typical software trace or log (for example, from Process Monitor) lists messages from several processes and threads sequentially. However, such columns may be split into individual process ID or thread ID columns. The same can be done for any Adjoint Thread and illustrated in the following diagram:

We call this analysis pattern Combed Trace by analogy with multibraiding.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

In Adjoint Message analysis pattern description we mentioned compressing message sequences having the same message attribute into one message. Considering the trace as “topological” space and message attribute as “equivalence” relation we introduce Quotient Trace analysis pattern by analogy with quotient space in topology. By endowing message sequences having the same attribute with some “metric” such as cardinality of Message Set we can also visually distinguish resulted quotient messages if they have the same attribute but from different sequences at different times. All this is illustrated in the following diagram:

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

By analogy with Adjoint Thread of Activity we introduce Adjoint Message analysis pattern. Most if not all analysis patterns focus on log message text and consider TID, PID, Module, source file and function as its attributes. However, we can choose one of attributes and consider it as a message in its own right with the original message text consigned now as another attribute. Then we can analyze the structure of the trace from the perspective of that newly selected message:

Since the number of different message values now is smaller (for example, module names) compared to normal trace messages we can use them in protein-like encoding and structure analysis schemes (see Software Trace and Logs as Proteins). We metaphorically name Adjoint Messages as Amino-acid-Messages (A-Messages). We can also compress same message sequences into one message which may be useful for pattern matching (and even use different color intensities to represent message cardinalities):

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

General traces and logs may have Message Space regions “surrounded” by the so-called Interspace. Such Interspace regions may link individual Message Space regions like in this diagram generalizing WinDbg !process 0 3f command output:

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Message stream can be considered as a union of Message Spaces. A message space is an ordered set of messages preserving the structure of the overall trace. Such messages may be selected based on a memory space they came from or can be selected by some other general attribute, or a combination of attributes and facts. The differences from Message Set is that Message Space is usually much larger (with large scale structure) with various Message Sets extracted from it later for fine grained analysis. This pattern also fits nicely with Adjoint Spaces. Here’s an example of kernel and managed spaces in the same CDF / ETW trace from Windows platform where we see that kernel space messages came not only from System process but also from other process contexts:

In the context of general traces and logs such as debugger logs separate Message Space regions may be linked (or “surrounded”) by Interspace.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Reading Boris Uspensky’s book “A Poetics of Composition: The Structure of the Artistic Text and Typology of a Compositional Form” (in its original Russian version) led me to borrow the concept of viewpoints. The resulting analysis pattern is called Trace Viewpoints. These viewpoints are, “subjective” (semantically laden from the perspective of a trace and log reader), and can be (not limited to):

- Error viewpoints (see also False Positive Error, Periodic Error, and Error Distribution)

- Use case (functional) viewpoints (see also Use Case Trail)

- Architectural (design) viewpoints (see also Milestones)

- Implementation viewpoints (see also Implementation Discourse, Macrofunctions, and Focus of Tracing)

- Non-functional viewpoints (see also Counter Value and Diegetic Messages)

- Signal / noise viewpoints (see also Background and Foreground Components)

In comparison, Activity Regions, Data Flow, Thread of Activity, and Adjoint Thread of Activity are “objective” (structural, syntactical) viewpoints.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

So far we have been discussing trace analysis patterns related to execution of a particular software version. However, software code changes and also its tracing and logging output: from large scale changes where components are replaced to small scale code refactoring affecting message structure and format. On a software narratological level this corresponds to a narrative about a software trace or log, it evolution. Such Meta Trace analysis pattern is different from Master Trace pattern where the latter is similar to what Metanarrative is usually meant in narratology: a master or grand idea - an expected trace if all functional requirements were correctly identified and implemented during software construction and non-functional ones are met during software execution.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Whereas, when naturally visualized with Dump2Picture, a CDF trace looks like a sea with embedded library, a process monitor log looks more like a fragment from a memory dump with some large scale internal structure:

Its 450×1280 slice:

Top and bottom of x4 magnified image:

…

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

The modern software trace recording, visualization and analysis tools such as Process Monitor, Xperf, WPR and WPA provide stack traces associated with trace messages. Consider stack traces as software traces we have, in a more general case, traces (fibers) bundled together on (attached to) a base software trace. For example, a trace message, that mentions an IRP can have its I/O stack attached together with thread stack trace with function calls leading to a function that emitted the trace message. Another example is association of different types of traces with trace messages such as managed and unmanaged ones. This general trace analysis pattern needs a name so we opted for Fiber Bundle as analogy with a fiber bundle from mathematics. Here’s a graphical representation of stack traces recorded for each trace message where one message also has an associated I/O stack trace:

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

When depicting trace analysis patterns I used two-dimensional diagrams based on CDF (ETW) traces such as this one for a bifurcation point:

While working today on a new pattern I needed a new expressive way to graphically illustrate the same idea of trace bifurcation points but without too much drawing. Traces from particle chambers and scattering diagrams came to my imagination after draw the first few diagrams illustrating bifurcation points:

Time directional arrow end can be omitted:

Trace variation after a bifurcation point can be indicated by angle partition:

The case when a variation also happens before is illustrated on this diagram:

and the case with several bifurcations:

Are N-bifurcations like on the diagram below possible?

Yes, they are, if the course of execution depends on some non-binary trace message parameter such as a loaded module that implements a required interface differently.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Counter Value pattern covers performance monitoring and its logs. A counter value is some variable in memory, for example, a module variable, that is updated periodically to reflect some aspect of state or it can be calculated from different such variables and presented in trace messages. Such messages can also be organized in a similar format as ETW based traces we usually consider as examples for our trace patterns:

Source  PID TID   Function         Value
=================================================
[…]
System    0   0   Committed Memory 12,002,234,654
Process 844   0   Private Bytes    345,206,456
System    0   0   Committed Memory 12,002,236,654
Process 844   0   Working Set      122,160,068
[…]

Therefore, all other trace patterns such as adjoint thread (can be visualized via different colors on a graph), focus of tracing, characteristic message block (for graphs), activity region, significant event, and others can be applicable here. There are also some specific patterns such as global monotonicity and constant value that we discuss with examples in subsequent parts.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

After writing about music for debugging and founding software narratology I decided to start writing about fiction. The first masterpiece is The Sound and The Fury by William Faulkner. I confess that I’m in love with Folio Society books and when I saw this color version (an original idea by Faulkner now fulfilled by modern printing technology) I immediately recognized its importance for software trace analysis:

http://www.foliosociety.com/book/SAF/sound-and-the-fury

I’m pretty sure Faulkner would have been delighted to see trace analysis patterns and how they may help in writing fiction.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

In a week this short full color book should appear in online bookstores:

• Title: Software Narratology: An Introduction to the Applied Science of Software Stories
• Authors: Dmitry Vostokov, Memory Dump Analysis Services
• Description: This is a transcript of Memory Dump Analysis Services Webinar about Software Narratology: an exciting new discipline and a field of research founded by DumpAnalysis.org. When software executes it gives us its stories in the form of UI events, software traces and logs. Such stories can be analyzed for their structure and patterns for troubleshooting, debugging and problem resolution purposes. Topics also include software narremes and their types, anticipatory software construction and software diagnostics.
• Publisher: OpenTask (April 2012)
• Language: English
• Product Dimensions: 28.0 x 21.6
• Paperback: 26 pages
• ISBN-13: 978-1908043078

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Memory Dump Analysis Services (DumpAnalysis.com) organizes a free Webinar about software narratology: an application of ideas from narratology to software narrative stories.

Learn about this exciting new discipline and a field of research. When software executes it gives us its stories in the form of UI events, software traces and logs. Such stories can be analyzed for their structure and patterns for troubleshooting, debugging and problem resolution purposes.

Prerequisites: basic software troubleshooting and ability to read software generated logs.

Audience: anyone can benefit from this new perspective: from software users to software engineers and managers.

Space is limited.
Reserve your Webinar seat now at:
https://www3.gotomeeting.com/register/603136294

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

The number of software trace analysis patterns approaches the critical mass of 50 and we have decided to focus on software tracing and logging in the forthcoming year. Some books on tracing including Volume 7 of Memory Dump Analysis Anthology will be published by OpenTask during that year and our efforts will be to further advance software narratology, software trace linguistics, and software trace analysis in the context of memory dump analysis, generative debugging and modeling software behavior.

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -

Memoretics as a science of memory snapshots borrows many ideas from the following disciplines (the list is not exhaustive):

• Troubleshooting and Debugging
• Intelligence Analysis
• Critical Thinking
• Forensics
• Linguistics
• Archaeology
• Psychoanalysis
• History
• Mathematics: Sets and Categories
• Literary Criticism and Narratology

It also contributes many ideas back. The following diagram depicts such an interaction:

- Dmitry Vostokov @ DumpAnalysis.org + TraceAnalysis.org -