Software Diagnostics Library

Generative AI LLM models such as GPT-4 are very good at producing Annotated Stack Traces. This is similar to Message Annotations trace and log analysis pattern. Below are two examples from ChatGPT:

Explain this stack trace fragment line by line:
06 ffffa784`8ee7db20 fffff804`0516bbf2 nt!IofCallDriver+0x55
07 ffffa784`8ee7db60 fffff804`0516b9d2 nt!IopSynchronousServiceTail+0x1d2
08 ffffa784`8ee7dc10 fffff804`0516ad36 nt!IopXxxControlFile+0xc82
09 ffffa784`8ee7dd40 fffff804`04e28775 nt!NtDeviceIoControlFile+0x56
0a ffffa784`8ee7ddb0 00007ffe`4d263834 nt!KiSystemServiceCopyEnd+0x25
0b 0000009b`cc0fedd8 00007ffe`4a893ffb ntdll!NtDeviceIoControlFile+0x14
0c 0000009b`cc0fede0 00007ffe`4bb95f91 KERNELBASE!DeviceIoControl+0x6b
0d 0000009b`cc0fee50 00007ff7`6544342f KERNEL32!DeviceIoControlImplementation+0x81

This stack trace appears to be from the Windows operating system, showing a series of function calls related to an I/O operation, specifically a device I/O control operation (DeviceIoControl). The stack trace is displayed in reverse chronological order, meaning the most recent call is shown first. Here’s a line-by-line explanation:

Lines 06 to 09 (nt!…): These lines are part of the Windows kernel (ntoskrnl.exe), which is the core of the Windows operating system. The prefix nt! indicates functions within the kernel.

IofCallDriver (line 06): A kernel function that forwards an I/O request packet (IRP) to a driver. The +0×55 indicates the offset within the function where the call occurred.
IopSynchronousServiceTail (line 07): A function that handles synchronous I/O operations. The +0×1d2 offset shows where in the function the operation occurred, indicating further processing of the IRP.
IopXxxControlFile (line 08): Likely a function that processes control operations (like IOCTL calls) on files. The +0xc82 is the offset where the call happened.
NtDeviceIoControlFile (line 09): A system service function that applications can call to execute device I/O control operations. The +0×56 offset indicates the point of execution within the function.
Line 0a (nt!KiSystemServiceCopyEnd): This is part of the system call dispatch mechanism, which is responsible for transitioning from user mode to kernel mode. +0×25 is the offset within this function.

Line 0b (ntdll!NtDeviceIoControlFile): This represents a call made from a user-mode application into the Windows Native API (NTDLL.dll) to perform a device I/O control operation. The +0×14 offset indicates the execution point within the function.

Line 0c (KERNELBASE!DeviceIoControl): This call originates from the KERNELBASE.dll, a Windows DLL that provides the base-level functionality for high-level APIs. The DeviceIoControl function is a wrapper that applications can use to send control codes to drivers. The +0×6b offset indicates where the function proceeds with the operation.

Line 0d (KERNEL32!DeviceIoControlImplementation): This is the implementation of the DeviceIoControl function in KERNEL32.dll, which is another layer of abstraction that eventually calls into the kernel-mode function to perform the operation. The +0×81 offset indicates the execution point within this implementation.

This stack trace illustrates how a device I/O control operation initiated by an application flows through various layers of the Windows operating system, from high-level APIs down to kernel-mode functions, to interact with a device driver. Each step in the process is crucial for understanding how the operating system processes I/O requests, handles system calls, and communicates with hardware.

Explain this stack trace fragment line by line:
00 msvcrt!vsnwprintf
01 wdscore!StringCchPrintfW
02 wdscore!CLogManager::LogA
03 wdscore!WdsSetupLogMessageA
04 cbscore!CBSWdsLog
05 cbscore!CCbsExecutionObject::AddPackage
06 cbscore!CCbsExecutionObject::AddPackage
07 cbscore!CCbsExecutionObject::AddPackage
08 cbscore!CCbsExecutionObject::RecallOrSupersedLowerVersion
09 cbscore!CCbsExecutionObject::ProcessMumServicing
0a cbscore!CCbsExecutionObject::AddPackage
0b cbscore!CCbsExecutionObject::Plan
0c cbscore!CCbsExecutionObject::GenerateExpressFileList
0d cbscore!CCbsSession::ActionListGenerateMissingFileList
0e cbscore!CCbsPublicSession::WritePackageFileList
0f RPCRT4!Invoke
10 RPCRT4!Ndr64StubWorker
11 RPCRT4!NdrStubCall3
12 combase!CStdStubBuffer_Invoke
13 RPCRT4!CStdStubBuffer_Invoke
14 combase!InvokeStubWithExceptionPolicyAndTracing::__l6::<lambda_ c9f3956a20c9da92a64affc24fdd69ec>::operator()
15 combase!ObjectMethodExceptionHandlingAction<<lambda_ c9f3956a20c9da92a64affc24fdd69ec> >
16 combase!InvokeStubWithExceptionPolicyAndTracing
17 combase!DefaultStubInvoke
18 combase!SyncServerCall::StubInvoke
19 combase!StubInvoke
1a combase!ServerCall::ContextInvoke
1b combase!CServerChannel::ContextInvoke
1c combase!DefaultInvokeInApartment
1d combase!ComInvokeWithLockAndIPID
1e combase!ThreadInvokeReturnHresult
1f combase!ThreadInvoke
20 RPCRT4!DispatchToStubInCNoAvrf
21 RPCRT4!RPC_INTERFACE::DispatchToStubWorker
22 RPCRT4!RPC_INTERFACE::DispatchToStubWithObject
23 RPCRT4!LRPC_SCALL::DispatchRequest
24 RPCRT4!LRPC_SCALL::HandleRequest
25 RPCRT4!LRPC_SASSOCIATION::HandleRequest
26 RPCRT4!LRPC_ADDRESS::HandleRequest
27 RPCRT4!LRPC_ADDRESS::ProcessIO
28 RPCRT4!LrpcIoComplete
29 ntdll!TppAlpcpExecuteCallback
2a ntdll!TppWorkerThread
2b KERNEL32!BaseThreadInitThunk
2c ntdll!RtlUserThreadStart

This stack trace seems to come from a Windows environment, and it illustrates the flow of a specific operation—likely a component-based servicing action (related to Windows Updates or system component management) that involves logging, package addition, and possibly dealing with an RPC (Remote Procedure Call) request. Let’s break down the stack trace line by line:

msvcrt!vsnwprintf: This function is part of the Microsoft Visual C Runtime Library. It’s a variant of snprintf that works with wide (Unicode) strings, used for formatted string operations.

wdscore!StringCchPrintfW: A function likely related to Windows Deployment Services (WDS) for secure string formatting into a buffer. StringCchPrintfW is a safer version of sprintf for wide strings.

wdscore!CLogManager::LogA: A method in the WDS core library for logging messages. This method suggests the operation involves ASCII string logging.

wdscore!WdsSetupLogMessageA: Another logging-related function in WDS, focused on setup log messages.

cbscore!CBSWdsLog: Indicates a function within the Component-Based Servicing (CBS) core library, specifically for logging in the context of WDS.

6-0a. cbscore!CCbsExecutionObject::AddPackage (lines 05 to 0a): Repeated calls to a method that adds a package to the CBS execution object. This could be part of an update or system component management operation.

cbscore!CCbsExecutionObject::RecallOrSupersedLowerVersion: A method that deals with recalling or superseding lower versions of a package or component.

cbscore!CCbsExecutionObject::ProcessMumServicing: A method likely involved in servicing manifest updates (MUM files), which are used in Windows servicing to describe updates.

cbscore!CCbsExecutionObject::Plan: Part of the planning phase in a CBS operation, where the system determines what actions to take with the packages.

cbscore!CCbsExecutionObject::GenerateExpressFileList: Generates a list of files for an express installation, which is a faster update method that only downloads necessary components.

cbscore!CCbsSession::ActionListGenerateMissingFileList: A method to generate a list of missing files based on the planned actions.

cbscore!CCbsPublicSession::WritePackageFileList: Writes out the list of files associated with the packages being serviced.

13-1e. RPCRT4 and combase (lines 0f to 1e): These lines involve the RPC runtime and COM base libraries, indicating that part of the operation was invoked over RPC, a mechanism that allows a program to execute code on another machine or process.

1f-2c. The stack trace concludes with system and thread initialization functions, like TppWorkerThread and BaseThreadInitThunk, which are part of the thread pool and thread startup routines in Windows. These functions are standard for threads executing in the Windows environment.

This trace is a deep dive into how Windows manages system components, specifically around updating or managing packages, and involves various subsystems like WDS, CBS, and RPC communications.

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

When comparing different traces from the same system we may see different correlations of Statement Densities. For example, when A message density is increased, then C message density is also increased regardless of any changes to B message density. We can borrow concentration notation from chemical kinetics and use [A], [B], and [C] for corresponding Statement Densities, either local in Activity Regions or globally for the whole trace. Observed correlations may point to existing causal mechanisms (like when kinetics points to reaction mechanisms):

This Message Kinetics pattern is more general than Relative Density where a semantic relationship is already known and the comparison is made between working and non-working scenarios. Variations of message densities may occur in normal scenarios, for example, with different amount of input data. There can be several types of Message Kinetics in one trace or log.

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

Trace Variance is the measure of variability of traces and logs in different environments. This trace and log analysis pattern metaphor is borrowed from variance in machine learning like Trace Bias. Here, different environments or the same environment at different times are considered as input data.

Other analysis patterns can be used to investigate Trace Variance such as Bifurcation Point, Trace String, and Polytrace.

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

Trace Bias is the measure of how faithfully traces and logs as a models of computation reflect the actual computation. The term bias has many meanings. This trace and log analysis pattern metaphor is borrowed from model bias in machine learning and statistics. Traces, and therefore, their biases range from Empty Traces to Sparse Traces to traces from Time Travel Debugging tools. How we do tracing, for example, via Declarative Traces or Moduli Traces, have direct impact on Trace Bias.

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

Trace Lattice is a selection of messages based on a fixed order distance between them (similar to lattices in geometry, one-dimensional in this case) or some other metric:

This analysis pattern is different from Time Scale where fixed time distance is used with additional analysis transformations.

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

Trace Pressure is a “thermodynamic” variable along with Trace Temperature and Trace Volume. The fourth variable, the number of non-Silent Messages, N, is obvious. In the following diagram, two parts of the trace have approximately the same volume but different “temperature” that result in approx. same “pressure” according to a metaphor of an ideal gas law:

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

Trace Temperature is an external quantitative (a number) or qualitative (for example, “hot”, “warm”, or “cold”) trace attribute of a trace or log or its fragment such as Activity Region that measures importance of the incident:

It is one example of Trace Field. Being an external attribute, independent from content, the trace with very few or no messages (Sparse Trace) can still have very high “temperature.” However, this analysis pattern is not the same as News Value which is an internal attribute based on trace content that measures its importance.

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

Trace Volume counts not only messages but also Silent Messages:

The volume metaphor can also be applied to parts of the trace such as Activity Regions, Threads (and Adjoint Threads) of Activity, and various braids.

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

How can we view traces and logs to facilitate there classification? One of the possible avenues is to view traces and logs consisting of the very large conceptual units, bricks, or bases to avoid combinatorial explosion. The natural candidates are Activity Region, Discontinuity, and Error Message or Exception Stack Trace Characteristic Message Block (Crash metaphor). So we have A, C, D, and we add B for Bad Activity Region whatever that means. For example, a typical trace that starts with some activity, then we have an exception stack trace belongs to AC Trace Class. Another trace that starts with some activity, then doesn’t show any messaged till the end can be classified as AD. A more elaborate class is ABCABCABCBC with periodicity of normal and bad activities culminating with error messages and finally with the last bad activity and exception. These three Trace Classes are depicted in the following diagram:

We combine sequences of the same bases into one, for example, AAAAA as just A, thus abstracting from semantic and syntactical differences between normal Activity Regions. The same is with other bases and can be considered as an extreme version of Quotient Trace. This classification can also be applied to individual Threads of Activity and Adjoint Threads of Activity.

This genetic-like description parallels the earlier proposal for DNA and RNA of Ruptured Computation.

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

If Trace String analysis patterns caters for trace length variations when the sequence of Activity Regions is constant then Trace Amplitude is about different paths between constant regions at both trace ends that may include varying regions in between, as depicted in the following diagram:

Therefore, several different Trace Strings may be embedded in one Trace Amplitude. The idea of this analysis pattern was borrowed from the path integral formulation of QM.

- 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 -

When comparing traces we may be interested in their Trace Benchmarks, for example (not limited to, just a proposal):

• Performance impact (impact this trace collection has on system performance)
• Problem relevance score (was it really relevant or missed the problem repro?)
• Easy to understand (is there any room for improvement for trace messages?)
• Sensitivity (does it have any sensitive information?)
• Canonicity score (does it contain all required ATIDs?)
• Novelty score (any surprises?)
• Sufficiency (does it require supplemental log from other tools?)
• Pattern metrics (number of distinct error messages, etc., based on trace and log analysis patterns)
• Problem resolution impact (was it the trace that provided missing insights?)

This post-analysis pattern may also be useful for problem postmortems and case studies. This pattern is different from Trace Summary which is a pre-analysis information.

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

When comparing traces, saving them for further processing, or making their Trace Mask, we may transform them into Canonical Trace form with the same Trace Schema:

This analysis pattern is different from Master Trace and Meta Trace, where the former is about an “ideal” archetype trace of a use case to compare to, and the latter is about trace evolution when underlying code changes.

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

Most trace and log diagrams we use for analysis pattern illustration are 1-dimensional if we ignore the structure of individual trace messages despite being looking as 2-dimensional. It is, therefore, natural to consider them as periodic and aperiodic tessellations of a line using a set of segments of different lengths and colors. A Trace Tessellation analysis pattern is such an arrangement based on some criteria, for example, a combination of trace and log analysis patterns or simply some A(TID) mapping.

GPT-4 suggests the following outline for 1-dimensional tessellation:

Tessellating a one-dimensional space like a line is fundamentally simpler than tessellating a two-dimensional space. Given that a line segment is the only basic shape you can use to tessellate a line, at first glance, it may seem there isn’t much room for diverse or “interesting” patterns. However, we can introduce variability by considering the following:

Varying Lengths: Instead of using uniform line segments, use segments of varying lengths. For instance, alternating between short and long segments can create a rhythmic pattern.

Color or Texture Variations: Even though it’s a one-dimensional space, you can vary colors or textures. For example, alternate between black and white line segments.

Spacing: While traditional tessellation has no gaps, you could introduce regular or irregular spacing between line segments for a more abstract approach.

Pattern Interruption: Repeating a set pattern and then deliberately interrupting it can be visually interesting. For instance, having three short segments followed by one long segment, and then repeating.

Associative Representation: You can use tessellated patterns on a line to represent data or other concepts. For example, Morse code uses a combination of short (”dots”) and long (”dashes”) signals, essentially tessellating a line with two types of segments to convey information.

Fractals and Recursive Patterns: While not “tessellation” in the traditional sense, you can create patterns on a line using recursive rules. The Cantor set is an example of this, where you take a line segment, remove the middle third, and then repeat the process for the remaining segments ad infinitum.

These are just a few ways to introduce interest and complexity into the “tessellation” of a one-dimensional space. While it’s fundamentally simpler than two-dimensional tessellation, with some creativity, you can still achieve intriguing and meaningful patterns.

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

Suppose we are interested in particular message types, which are Message Patterns. We can form a Pattern Vector with values of corresponding Message Pattern occurrences during each fixed time interval corresponding to selected Time Scale. A typical example is illustrated in the following diagram with counted Silent Messages as well:

Each Message Vector component value is the local Statement Current. We can also select Statement Densities instead, especially if we select only Message Patterns from Foreground Components. Other values are possible, for example, from Trace Field.

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

Inspired by Laws of Form and works inspired by it including Story and Structure we introduce Iconic Trace symbolic mapping for individual messages, Activity Regions, Motives, and Activity Theatre. A typical example is illustrated in the following diagram:

It is also possible to construct Iconic CoTraces. We will provide different symbolic profiles (with the meaning of various symbols) and examples later in the forthcoming revision of Software Narratology.

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

Often, we have Basic Facts but are unsure 100% if particular trace messages are relevant or not. We take the idea of Case Messages analysis pattern from fuzzy sets where we have a degree of membership function. It is illustrated in the following diagram:

In comparison, Message Set analysis pattern is about crisp sets where the degree of membership is either 0 or 1.

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

If necessary, for example, for the conversion to Text Trace, individual trace messages may need to be converted to message text form blending various message constituents like ATIDs, Message Invariants, data, and Trace Constants into some textual narrative form. We illustrate it in the following diagram:

We call the resulting message text Denormalized Message. It is the reverse process than finding Features of Activity and constructing structured messages conforming to some Trace Schema.

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

In addition to Trace Skeleton, we can insert Silent Messages, treat non-silent messages as instances of some template message, and even take Quotient Trace of them, leaving the position of final non-silent messages intact. This is similar to retraction in topology, so we name this analysis pattern Trace Retract and illustrate it in the following diagram:

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

When we construct Trace Molecule, we may find that some Message Complex links from different ATIDs point to the same message and Tracemes. In such a case we have Message Bond by analogy with covalent bonds. One such example is illustrated in the following diagram:

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