venom VS gdb-dashboard

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.

Disclaimer: I'm making a competing build system.

I won't tell you specific build systems, but I will tell you what to look for.

Look for power. Unlimited power. [1]

Usually, this means a few things:

1. The build system uses a general-purpose language, even if the language needs features to be added.

2. The build system does not reduce the power of the general-purpose language. For example, say it starts with Python but prohibits recursion. In that case, you know it is not unlimited power. Looking at you, Starlark.

3. The build can be dynamically changed, i.e., the build is not statically determined before it even begins.

4. Each task has unlimited power. This means that the task can use a general-purpose language, not just run external processes.

5. And there has to be some thought put it in user experience.

Why are these important? Well, let's look at why with CMake, which fails all of them.

For #1, CMake's language started as a limited language for enumerating lists. (Hence, CMakeLists.txt is the file name.) And yet, it's grown to be as general-purpose as possible. Why? Because when you need an if statement, nothing else will do, and when you need a loop, nothing else will do.

And that brings us to #2: if CMake's language started limited, are there still places where it's limited? I argue yes, and I point to the article where it says that your couldn't dynamically call functions until recently. There are probably other places.

For #3, CMake's whole model precludes it. CMake generates the build upfront then expects another build system to actually execute it. There is no changing the build without regenerating it. (And even then, CMake did a poor job until the addition of `--fresh`.) A fully dynamic build should be able to add targets and make others targets depend on those new targets dynamically, among other things.

For #4, obviously CMake limits what tasks can do because Ninja and Make limit tasks to running commands.

As another example, to implement a LaTeX target, you technically need a while loop to iterate until a fixed point. To do that with Make and Ninja, you have to jump through hoops or use an external script that may not work on all platforms.

CMake obviously fails #5, and to see how much other build systems fail it, just look for comments pouring hate on those build systems. CMake fails the most, but I haven't seen one that passes yet.

As an example, CMake barely got a debugger. Wow! Cool! It's been 20 years! My build system will have a debugger in public release #2 (one after the MVP) that will be capable of outputting to multiple TTY's like gdb-dashboard. [2] They should have had this years ago!

Should other comments suggest specific build systems, like the one that suggested Bazel, judge them by this list. Some will be better than others. None will pass everything, IMO, which is why I'm making my own.

[1]: https://youtube.com/watch?v=Sg14jNbBb-8

[2]: https://github.com/cyrus-and/gdb-dashboard

To debug, termdebug is built in to vim and supports a front end to gdb. In combination with gdb-dashboard this gives a reasonably good debugging experience

https://github.com/cyrus-and/gdb-dashboard, https://github.com/cyrus-and/gdb-dashboard/wiki

You can use the Python API to GDB to get really fancy TUIs like this: https://github.com/cyrus-and/gdb-dashboard

Try GDB Dashboard, it makes gdb much easier to use:

https://github.com/cyrus-and/gdb-dashboard

There's also Voltron which works with both gdb and lldb (amongst others):

https://github.com/snare/voltron

I can't believe no one has mentioned `gdb-dashboard` [1] yet! I use it extensively. [2]

Beyond that, I have recently learned how to write custom pretty printers for GDB. This saves a lot of screen space. I should probably update [2] soon with those new techniques.

GDB is powerful, useful, and after getting my start in IDE debuggers, including Visual Studio, I struggle whenever I have to go back.

[1]: https://github.com/cyrus-and/gdb-dashboard

[2]: https://gavinhoward.com/2020/12/my-development-environment-a...

Debugging: Learn gdb. You can use it for assembly. You can use it for C on a MCU. You can use it for Go on a Linux system. It’s ubiquitous, versatile, and worth understanding. You can pretty-print the output with something like this to help you out at first. There are 2 things you need, however: on chip debugger and a debug probe (though you don’t need an expensive one). Effectively, you talk to GDB, GDB talks to the server exposed by OCD, OCD knows the debug probe protocol, and the debug probe can use the MCU debug peripheral via SWD or JTAG to get those details.

I use gdb because I'm usually in the terminal. I don't think terminal it's any faster though. This is a nice addition to boring old terminal gdb.