godog VS venom

Quick side note - this is probably the subject of a whole other blog post but my current favourite way of testing CLI tools uses godog to write tests. It can be a little complex but I've found it supremely powerful. Here are some good examples of how I've approached it with layli and wait-for.

It's complaining about a file missing in $GOPATH/pkg... but I'm not sure how do I get the packages its asking for. Things I have tried: - running go mod tidy - running go get -u github.com/cucumber/godog (hoping it would add the files to pkg/mod/... - Uninstalled and installed Go again.

I also use BDD (Gherkin with godog in particular) to verify and document the expected behaviour of a product from an end user's perspective when needed. I usually do this when the product also contains untested code that I have no control over when I'm working on a problem - this gives me peace of mind over something I can't control while doubling as documentation.

It looks like it is not possible to share steps between scenario's or features. In https://github.com/cucumber/godog it is possible to share steps.

Differences between gherkingen and godog are:

You can also load fixtures in database directly, work with Kafka queues both as a producer (e.g. write an event to a Kafka queue, wait a few seconds and see that it was consumed by the service you test, and that some side effects can be observed) or as a consumer (e.g. make sure after an HTTP call, an event was correctly pushed to a queue), or even read a mailbox in IMAP to check that your service correctly send an email.

It's a bit rough on the edges sometimes, but I'd never go back on writing integration tests directly in my programming language. Declarative is the way to go.

[1]: https://github.com/ovh/venom

To write and run our integration tests, we'll use Venom. Venom is a tool created and made open-source by OVHcloud: https://github.com/ovh/venom

From my experience, generated tests are worthless for anything more serious than smoke tests. I prefer working with no tests than automated tests, I feel they give you a false sense of confidence.

The Step CI engine itself looks good though. It looks like a cleaner, but less powerful version of a tool (open source, build in-house) we used when I worked at OVHcloud, Venom: https://github.com/ovh/venom

Here's an example test file for the HTTP executor of Venom: https://github.com/ovh/venom/blob/master/tests/http.yml it's very close to Step CI format.

I'd still use Venom because it's way more powerful (you have DB executors for example, so after executing a POST request you can actually check in DB that you have what you expect) and I prefer focusing on actually writing integration tests instead of generating them.

Maybe this post sounds harsh (I feel it as I write it because I have strong feelings against test generation) but I think your approach is a good one for actually writing automated tests. Testing APIs declaratively like this has a great benefit: your tests work on an interface. You can migrate your API to a whole new stack and your tests remain the same. I did it multiple time at OVHcloud: one time migrating a huge API from a Go router to another (Gin->Echo), and another time migrating public APIs from a legacy, in-house Perl engine to a Go server.

I still struggle with GDB but my excuse is that I seldom use it.

When I was studying reverse engineering though, I came across a really cool kit (which I've yet to find an alternative for lldb, which would be nice given: rust)

I'd recommend checking it out, if for no other reason than it makes a lot of things really obvious (like watching what value lives in which register).

https://github.com/hugsy/gef

LLDB's closest alternative to this is called Venom, but it's not the same at all. https://github.com/ovh/venom

We incorporated [Venom](https://github.com/ovh/venom) into our workflow. It's great for initiating and managing a suite of yaml based tests. It didn't work out of the box for us due to the heavily asynchronous nature of our system, but after a few additions, it has helped my team greatly. We were often afraid to make large changes to critical pieces of the system since a full regression test could take a week or so to check everything. Now it takes an hour.