They should take single matcher during construction, and their match method should take a generic range, using the new generic matcher support.

Expected usage:

REQUIRE_THAT(get_keys(), AllMatch(KeyPattern({1, 2, 3})));
REQUIRE_THAT(get_numbers(), NoneMatch(IsOdd{}));
REQUIRE_THAT(get_strings(), AnyMatch(Contains("webscale") && !Contains("MongoDB")));

the matching semantics

We've had these four methods as far back as NUnit source history goes. The double? overloads are the same as the first two overloads except that they throw InvalidOperationException if you pass null. Since they can't be used to pass null, they don't do anything that the safer double overloads don't do.

Should we obsolete them or fix them? What would fixing them look like?

name

Issue Classification

• Feature request

Feature Request

Currently, the error messages when a consumer test fails to match the expectations are not very detailled. Many users don't realise they can look in the log to get more detailed errors.

We should improve this behaviour - at a minimum, we could add an error message directing users to the log file.

Ideally, we'd have a d

I would like to drop Spring Boot support for 1.5.x. It is EOL https://spring.io/blog/2018/07/30/spring-boot-1-x-eol-aug-1st-2019
I would also add some notes in the docs, that we adhere to the spring boot lifecycle in order to be transparent about dropping support for versions.

TravisCI (org) is dropping support for OSS builds at the end of this month, meaning they must be transferred to travis-ci.com, which uses a credit system. Builds may fail (and already are) if the number of credits is exhausted within th emonth.

We are recommending switching to GH Actions, but the choice of CI provider is up to the project maintainer.

See also:

• https://mailchi.mp/3d439ee

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