cats VS orange

Out of this frustration I've built: https://github.com/Endava/cats. It's for APIs, but mostly addressing exactly this case: don't use strings for everything, if you choose to use it though, make sure you add patterns for checking if things are valid, make sure you think about all the corner cases and all the weird characters that can brake you app, and so on.

If the API has an OpenAPI spec available, you can use: https://github.com/Endava/cats

This is why we need better tools which will give benefits for the added complexity. If you need to create both the feature files AND the code, it's just complexity with little benefits. But frameworks like https://github.com/karatelabs/karate or https://github.com/Endava/cats are hiding this complexity and remove the code layer entirely. Which, in my view, this is where you need to be in 2023, particularly for API testing.

I've built a tool specifically to test if these kind of characters will reach API backends: https://github.com/Endava/cats. My idea was that APIs should explicitly reject or sanitise input containing such characters.

Cats by Endava

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.