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Josh McKenzie commented on CASSANDRA-15585:
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{quote}automation – are we running tests regularly with the tools we built so
that they have the potential to find things:
{quote}
Currently the Jenkins / Circle straddle seems pretty unhealthy / unclear for
new contributors. We should probably figure this out (orthogonal to this
ticket, just thought about it reading your comment).
Definitely think any new heavier test suites we're writing (Harry, fallout,
else) we'll want to get into less frequent cadenced builds (thinking nightly
runs, weekly runs) and figure out the right way to raise signals to the dev
community that things are awry. Not sure if nagging slack bot or emails to
subbed w/a specific ML is the right answer. Also not sure what infra that
should run on (ASF Jenkins or something paid for or hosted by a set of
contributors but openly configured ??)
{quote} the project you mentioned
automated cassandra-diff framework
{quote}
In my ideal utopian world, we'd be running a suite of diffing tools with real
user schemas nightly between trunk and a last released correct build and
hitting the alarm klaxons when things diverge in slack. That project is super
nascent but it has the bones of what we need and all of us are smarter than
just a few of us; should have something to talk to the list about within a week
or so when ducks are in a row.
> 4.0 quality testing: Test Frameworks, Tooling, Infra / Automation
> -----------------------------------------------------------------
>
> Key: CASSANDRA-15585
> URL: https://issues.apache.org/jira/browse/CASSANDRA-15585
> Project: Cassandra
> Issue Type: Task
> Components: Test/dtest/python
> Reporter: Josh McKenzie
> Priority: Normal
> Fix For: 4.0-beta, 4.0-triage
>
>
> Reference [doc from
> NGCC|https://docs.google.com/document/d/1uhUOp7wpE9ZXNDgxoCZHejHt5SO4Qw1dArZqqsJccyQ/edit#]
> for context.
> *Shepherd: Jordan West*
> This area refers to contributions to test frameworks/tooling (e.g., dtests,
> QuickTheories, CASSANDRA-14821), and automation enabling those tools to be
> applied at scale (e.g., replay testing via Spark-based replay of captured FQL
> logs).
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