keploy VS ginkgo

October is the month of Open Source and Keploy is taking part in this celebration. You can contribute to several Keploy projects by participating in this year’s Hacktoberfest. You can both contribute to the code part and the no-code part as well. Here are some contributions that you can make!

curl --silent --location "https://github.com/keploy/keploy/releases/latest/download/keploy_linux_amd64.tar.gz" | tar xz -C /tmp sudo mkdir -p /usr/local/bin && sudo mv /tmp/keploy /usr/local/bin && keploy

Keploy is an open-source, end-to-end (E2E) testing toolkit for developers. It creates test cases and data mocks/stubs by recording API calls, database queries, etc., making releases faster and more reliable. Keploy works by being added as a middleware to your application. It captures and replays all network interaction served to the application from any source. This allows Keploy to generate test cases for all of your API endpoints, including those that are not explicitly tested by your unit tests. This can help you to identify and fix bugs that would otherwise go undetected. Keploy can create data mocks/stubs for your APIs, which can help you to isolate your tests and make them more reliable. It can automatically compare test cases generated from previously collected traffic against updated behaviour of your application, and bring any differences to your attention. This can help you to identify regressions in your production code early on.

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I have once contributed to one similar project https://github.com/keploy/keploy , This can help you generate e2e tests and mocks as well , with real api and infra calls .

Please feel free to checkout keploy too - https://github.com/keploy/keploy

This looks very similar to keploy but specific to node apps. Keploy is designed to be multi language.

I recently discovered Keploy (https://github.com/keploy/keploy) and was really impressed by its promise that it can generate API tests with code coverage by itself just by recording API calls and mocking everything that goes outside of the application - including calls to external API endpoints and the database. It seems like a super useful tool and potentially a game changer, especially now that GPT is starting another wave of automation.

Ginkgo: a testing framework based on the concept of ‌"Behavior Driven Development" (BDD)

We were also able to leverage Ginkgo's parallel testing runtime to run our integration tests on multiple concurrent processes. This provided multiple benefits: we could run our entire integration test suite in under 10 minutes and also reuse the same suite to load test the operator in a production-like environment. Using these tests, we were able to identify hot spots in the code that needed further optimization and experimented with ways to save API calls to ease the load on our own Kubernetes API server while also staying under various AWS rate limits. It was only after running these tests over and over again that I felt confident enough to deploy the operator to our dev and prod clusters.

A bit off-topic, but i really like the ginkgo BDD framework

You obviously are not familiar with the third circle of golang continuous integration hell that is ginkgo+gomega:

https://onsi.github.io/ginkgo/#adding-specs-to-a-suite

It’s actually worse than that example suggests. Stuff like Expect(“type safety”).ShouldBe(GreaterThan(13)) throws runtime errors.

The semantics of parallel test runs weren’t defined anywhere the last time I checked.

Anyway, you’ll be thinking back fondly to the days of TestShouldReplaceChildrenWhenUpdatingInstance because now you need to write nested function calls like:

Context(“instances”, func …)

Describe(“that are being updated”, …)

Expect(“should replace children”, …)

And to invoke that from the command line, you need to write a regex against whatever undocumented and unprinted string it internally concatenates together to uniquely describe the test.

Also, they dump color codes to stdout without checking that they are writing to a terminal, so there will be line noise all over whatever automated test logs you produce, or if you pipe stdout to a file.

If you are using Ginkgo for your e2e, this library might of help.

https://onsi.github.io/ginkgo/ offers a simple way to create tables with different scenarios useful to generate different test cases based on a file like a yml without to need to develop useless code. Maybe at start seems to be a little verbose but depends how you design the test case.

What are people’s thoughts on testing frameworks? I’ve heard that most devs only use the testing package in the standard library and the testify package for assertions— I assume this is because Go is meant to be lightweight and scalable, and adding external dependencies basically goes against that. But I’ve also seen devs use packages like ginkgo to make tests more structured and readable. What do you guys think?

Ginkgo Behavioural test framework

If you write your tests with Ginkgo [0] its CLI can do this for you. It also has nice facilities to quickly disable a test or portion of a test by pretending an X to the test function name, or to focus a test (only run that test) by prepending an F. It’s pretty nice.

[0]: https://onsi.github.io/ginkgo/

The BDD testing framework Ginko [1] has some "weird" / unidiomatic patterns, yet it is very popular

https://github.com/onsi/ginkgo