Problems faced when downstream testing Python packages

Downstream testing refers to the testing of software package done by their redistributors, such as Linux distributions. It could be done by distro-specific CI systems, package maintainers or — as it frequently is the case with Gentoo — even distribution users.

What makes downstream testing really useful is that it serves a different purpose than upstream testing does. To put it shortly, upstream testing aims to ensure that the current code of the package works in one or more reference environments, and meets quality standards set by the package authors. On the other hand, downstream testing aims to ensure that a particular version of the package (possibly an old one) works in the environment that it will be used on, or one that closely resembles it.

To put it another way, downstream testing may differ from upstreaming testing by:

  • testing a different (possibly old) package version
  • using a diferent (possibly newer) Python version
  • testing against different dependency versions
  • testing against an environment with additional packages installed (that may interfere unexpectedly)
  • testing on a different operating system, architecture, hardware, setup

While these may sound inconvenient and sometimes cause false positives, they have proven in the past to detect issues that went unnoticed by upstream and that could have broken production setups. Downstream testing is important.

Unfortunately, many test suites make assumptions that cause problems for downstream testers. Some of them can be worked around easily, others can not. In this article I’d like to discuss a number of these issues.

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