Gain insights into diagnostic techniques and anomaly detection with over 200 trace and log analysis patterns, applicable across various software environments, operating systems, and platforms.

General trace and log analysis patterns allow the application of uniform diagnostics and anomaly detection across diverse software environments. It includes troubleshooting, debugging, describing, and explaining problems and incidents on various operating systems and platforms. It is also helpful in recording network traces, malware, general data, narratives, and text and image analysis (space-like narratology). This course, organized as a pattern language dictionary and cross-referenced with classification, explains and teaches more than 200 traces and log analysis patterns.

A Guide to Learning Software Trace and Log Analysis Patterns

# Sequence repeat anomaly
Sometimes we have [periodic message blocks](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/periodic-message-renormalisation-significant) of a few adjacent messages, such as when flags are translated into separate messages per bit. Then we may have a pattern of **sequence repeat anomaly**. In this, one of several message blocks has either fewer or additional messages, while we expected it to be identical to other message blocks. Then, [missing
message](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/milestones-missing-opposition-original-ornament), [message context](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/annotations-change-context-cover-directory) may be explored further. The following diagram illustrates the pattern:

The name of the pattern comes from the notion of repeated DNA sequences.

# Time scale
A software trace or log can be analyzed using different **time scales**. The coarser the scale, the more messages are included in time intervals. Such per-interval [message sets](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/flow-interleave-invariant-pattern-set) can be analyzed and transformed into one message using analysis patterns such as [significant event](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/significant-silent-singleton-split-surveyor), [motivic trace](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/intra-correlation-macrofunction-motif-motivic-trace), [background and foreground components](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/foreground-characteristic-cotrace-critical-event), and [renormalization](https://www.educative.io/courses/guide-learning-software-trace-log-analysis-patterns/periodic-message-renormalisation-significant). The resulting new trace is a scaled version of the original trace, as depicted in the following diagram:

This lesson introduces the following analysis patterns: sequence repeat anomaly, time scale, trace flux, trace frames, trace viewpoints, and use case trail.

Getting Started

Activity

Block and Data

Error and Large Scale

Message 1

Message 2

Trace as a Whole

Trace Set

Vocabulary, Memory, Code and Text

Conclusion

Index

Repeat, Scale, Flux, Frames, Viewpoints, Use Case

Sequence repeat anomaly

Time scale