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Abstract

You now have enough background material that I can introduce debugging architecture patterns in this chapter. These are solutions to common problems of how to proceed with debugging. These patterns answer the most important questions of where, when, and how we debug and what we debug based on the debugging analysis patterns. The diagnostics and debugging “questions” pattern language thinking originated from memory and trace acquisition patterns. There are many ways to answer such questions and propose debugging strategies and methods; sometimes, you may even need to combine different approaches. However, there are the most important decisions to make before any debugging is done, and we name them debugging architecture patterns vs. debugging design patterns, which can be many for the particular architecture pattern. All debugging architecture patterns can be combined to form pattern sequences and pattern networks to solve complex problems of software behavior.

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Notes

  1. 1.

    Dmitry Vostokov, Theoretical Software Diagnostics: Collected Articles, Third Edition, 2020 (ISBN-13: 978-1912636334), pp. 176-177

  2. 2.

    Frank Buschmann et al., Pattern-Oriented Software Architecture: On Patterns and Pattern Languages, 2007 (ISBN-13: 978-0471486480)

  3. 3.

    https://en.wikipedia.org/wiki/In_papyro

  4. 4.

    https://en.wikipedia.org/wiki/In_vivo

  5. 5.

    Dmitry Vostokov, Proof of Concept Engineering Patterns, Memory Dump Analysis Anthology, Volume 15, 2023 (ISBN-13: 978-1912636150), pp. 284 - 285

  6. 6.

    https://en.wikipedia.org/wiki/In_vitro

  7. 7.

    https://en.wikipedia.org/wiki/In_silico

  8. 8.

    https://en.wikipedia.org/wiki/In_situ

  9. 9.

    https://en.wikipedia.org/wiki/Time_travel_debugging

  10. 10.

    Dmitry Vostokov, Software Narratology: A Definition, Theoretical Software Diagnostics: Collected Articles, Third Edition, 2020 (ISBN-13: 978-1912636334), p. 58

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Vostokov, D. (2024). Debugging Architecture Patterns. In: Python Debugging for AI, Machine Learning, and Cloud Computing. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9745-2_8

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