Description

I have a hardware abstraction layer interface, as well as a driver interface. I have multiple drivers utilizing the HAL interface, while implementing the driver interface. This plugin approach causes me to have identically named tests across multiple test files.

For example:

TEST_CASE("init() returns `E_HAL_INIT`, when HardwareAbstractionLayer::init() returns an er

This is a corner case that I only ran into in due to work on the analyzers in nunit/nunit.analyzers#54, and my guess is that very few will run into this problem. Nevertheless, I think we could improve the error message, and fix seems simple and safe.

Given a generic test method with no parameters we get a failure that is difficult to interpret for the users (see last example below for more deta

Please add support for the following keys to be used in the consumer version selectors when fetching pacts for verification:

{ "deployedOrReleased": true }

{ "deployed": true }

{ "released": true }

{ "environment": "<env name>" }

{ "consumer": "<name>" } // this may already be supported, please add it if not

These keys can be used in various combinations ( eg. `{ "env

When doing gamedays we usually write down the exact moment we started the experiment.
Usually we enable/disable chaos monkey with the endpoints /chaosmonkey/enable and /chaosmonkey/disable. The response could extended with a human readable timestamp.

So the response has a small benefit.

Please add support for the following keys to be used in the consumer version selectors when fetching pacts for verification:

{ "deployedOrReleased": true }

{ "deployed": true }

{ "released": true }

{ "environment": "<env name>" }

{ "consumer": "<name>" } // this may already be supported, please add it if not

These keys can be used in various combinations ( eg. `{ "envir

Feature request

Is your feature request related to a problem? Please describe

The analysis.py script provides just one confidence interval: 95%. While that is related to the 2-sigma interval of normal distribution, so in theory allows for easy estimation of bigger confidence intervals (like 3, 4, 5-sigma), it assumes a normal distribution, which the differences don't follow, and may be