Archive for the ‘Trace Analysis and Physics’ Category

Trace Analysis Patterns (Part 189)

Friday, July 17th, 2020

The relations between Causal History messages (0-chains) can be abstracted as Causal Chains (1-chains). Two relations can be linked if an endpoint of one is also a beginning point of another:

The relations of 1-chains can be abstracted as 2-chains and so on (n-chains):

We took the idea of relation spaces and chains from already quoted “Discreet Causal Theory” book. Causal chain terminology is also used in philosophy.

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 188)

Thursday, July 16th, 2020

When looking at Causal History we can choose Causal Messages (not necesseraly the top ones):

Causal Messages may not overlap with the trace Defect Group which may not have any causal relevance being only correlation messages.

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 187)

Wednesday, July 15th, 2020

Trace Paths and Back Traces form Causal History of the log where arrows point in the direction of possible causation:

Here we borrow the notion of causal sets from physics and corresponding mathematics. The left diagramming idea was taken from Discrete Causal Theory book and Hasse diagrams (which is inverted in our picture). Also, such graphs are internal to software narratives compared to the more general external space we proposed earlier.

We omit Time arrow as it is possible to consider general traces and logs with their causality markers.

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 180)

Thursday, October 17th, 2019

Traces and logs may show drastic qualitative and quantitative pattern changes. We call this analysis pattern Phase Transition by analogy with phase transitions in physical, biological and other complex systems. Usually the goal of the analysis is to find a parameter that caused such transition. Here are a few examples which are self-explanatory.

Change in Statement Density and Current:

Sudden proliferation of (Adjoint) Threads of Activity:

Discontinuity of Activity Region with only Drone Messages left:

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 171)

Wednesday, July 10th, 2019

Log message “frequencies” in time domain (Statement Current part) are addressed by Fourier Activity analysis pattern. However, we may have varying message density (Statement Density part) across different trace runs (space domain, irrespective of time irregularities):

According to OED, in computing and mathematics a string means “a linear sequence of records or data” and “a sequence of symbols or linguistic elements in a definite order”. So we propose to name this pattern that analyzes densities of messages or Activity Regions as Trace String. Such longitudinal “vibrations” can be compared and analyzed for anomalies across different log runs (Inter-Correlation) or across similar regions in the same log (Intra-Correlation). Here we consider message density change as a one-dimensional displacement. The analogy for this pattern came from one-dimensional vibrating strings (dual resonance model from 1969-70 by Nambu, Nielsen, and Leonard Susskind).

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 158)

Thursday, June 28th, 2018

Using the metaphor of renormalization from physics we introduce Renormalization trace and log analysis pattern where a selected message and its Message Context are replaced by a single message:

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 137)

Monday, December 12th, 2016

Recently we found a correlation between software trace with high Statement Density and Current of Periodic Error with uniform Error Distribution and process heap Memory Leak suspected from  memory dump analysis. If we metaphorically view periodic errors as “frequency” and the size of a heap as “mass” we may see that the growth of “frequency” correlates with the growth of “mass” and vice versa. Since frequency is inversely proportional to wave length we see a metaphorical analog to Louis de Broglie’s wave-particle duality. In general, as we already pointed in the discussion of narrativity and spatiality of software execution artifacts (see also Software Trace and Memory Dump Analysis seminar), software traces / logs and memory dumps can be seen as “dual” to each other according (metaphorically again) to de Broglie’s “duality of the laws of nature”. So we name this analysis pattern De Broglie Trace Duality since some memory dump regions can be considered of a general trace nature. Our correlation can be depicted in this diagram:

Practically, when we see Memory Consumption Patterns (but don’t know their source / root cause yet) we can ask for traces and logs, and in the case of frequent Periodic Errors found there we can suggest troubleshooting steps that may serve as a resolution or workaround.

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 136)

Wednesday, November 23rd, 2016

Considering and extending Trace Dimension to TID, ATID, and Time we use Poincaré section and Poincaré map analogies to introduce Poincaré Trace analysis pattern. We choose a value of one “coordinate”, for example, Time, and then form the new trace that consists of messages from different (A)TIDs that have the same timestamp as Time value or the last message(s) from other different ATIDs if they happened before section Time:

If we choose a different ATID than Time and TID, then Poincare Trace is equivalent to Adjoint Thread of Activity. If ATID is TID we have Thread of Activity as a trivial case. If we have Thread of Activity, we can choose some ATID and get Poincaré Trace as illustrated in the following diagram where the thick black line in the right Poincaré Trace represents Discontinuity and its Time Delta:

- Dmitry Vostokov @ + -

Trace Analysis Patterns (Part 93)

Sunday, November 9th, 2014

Sometimes we have several use case instances traced in one log file. Messages and Activity Regions from many Use Case Trails intermingle and make analysis difficult especially with the absence of UCID (Use Case ID), any other identification tags, or Message Links. However, initially most of the time we are interested in a sequence of Significant Events. After finding Anchor Messages we can use Time Deltas to differentiate between trace statements from different Use Case Trails. Here we assume correct Event Sequence Order. We call this pattern Event Sequence Phase by analogy with wave phases (all such individual “waves” may have different “shapes” due to various delays between different stages of their use case and implementation narratives):

In the picture above we also identified Dominant Event Sequence for use case instance C.

- Dmitry Vostokov @ + -