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# Testing Network Automation Data Transformation « ipSpace.net blog

**[ipSpace.net Blog](https://daily.dev/sources/ipspace)** · 4 min read · 0 upvotes · 0 comments

## Summary

Network automation solutions require transforming high-level user data models into low-level device data models, and testing this transformation logic is critical. Using the netlab tool as a case study, the post describes a multi-layered testing strategy: pre-commit tests run yamllint for YAML validation, pre-push tests run mypy for Python type checking plus ~100 topology transformation tests and ~100 error-handling tests. The test harness reads a lab topology, runs the transformation, and compares output against expected results. Code coverage tooling ensures core components are well-exercised. Over 150 integration tests are also run through the transformation engine on every pull request. For teams using external sources of truth like Nautobot, the recommended approach is to split data extraction and data transformation into separately testable phases.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.ipspace.net/2024/05/network-automation-testing>

## Questions this post answers

### How do I test data transformation logic in a network automation tool like netlab?

The netlab approach uses a simple test harness: read a lab topology file, run the core transformation code, then compare the output against stored expected results. Around 100 topology transformation tests and 100 error-handling tests cover the main scenarios. New test cases are added for every major bug fix, starting from the minimal topology that triggered the bug. Code coverage tooling then confirms that core components are exercised.

_Network automation engineers tracking transformation test patterns find related approaches on daily.dev._

### How should I structure tests when my network automation source of truth is an external database like Nautobot instead of YAML files?

Split the pipeline into two independently testable phases: data extraction and data transformation. Test extraction using a sample database with expected outputs. Test transformation using numerous YAML-based test scenarios focused on small subsets of functionality. This divide-and-conquer approach makes it far easier to pinpoint which part of the pipeline is broken compared to one giant end-to-end test case.

_Teams choosing between YAML-based and database-backed sources of truth for automation pipelines discuss trade-offs on daily.dev._

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---

Tags: [#python](https://daily.dev/tags/python), [#testing](https://daily.dev/tags/testing), [#networking](https://daily.dev/tags/networking), [#data-analysis](https://daily.dev/tags/data-analysis)

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