Tests are good. Generative tests are better. But we don't write them often enough. Why not?

Generative testing isn't new; John Hughes' QuickCheck is now implemented in 30 languages. Yet, it is still not an every day tool for most software teams. Why not?

I have seen that maintaining a generative, or simulation testing suite can require the full-time attention of a few senior engineers. And over time, the tests become complex, brittle, ineffective, and hard to diagnose.

But isn't generative testing supposed to make testing simpler, and more effective?

I believe that it requires a rather different paradigm of thought from what we're used to with traditional testing. And recognising this paradigm shift can help us leverage it effectively.

I'll discuss a holistic perspective of generative testing to elucidate this thought paradigm.

Using a practical scenario, I'll discuss the patterns that emerge from this thinking, and how they address the concerns of making generative tests more effective, and maintainable.

We recently started working on a new Elixir project and had the need for a background processing system.

We decided to build our own background processing system in Elixir.

This talk describes why and how we built our own background processing system in Elixir.

How we use Elixir's GenStage package for adding back-pressure between the producer and consumer stages and how we add support for custom rate-limiting between several pipelines.

In this talk, we will cover our journey from the base to the summit of a masterless multi-node distributed system in Elixir. We’ll talk about the challenges faced, decisions made and how they shaped/evolved the architecture.

DSLs allow us to express our code in verbs and nouns that describe our domain. This helps keeping business logic clean of implementation specific details, and allows for more readable code. Few languages have support for easily defining DSLs, for example macros in clojure/lisp, metaprogramming in ruby, but these techniques are not portable to other languages. Free monads is a way to convert a functor into monads, and allow us to create a structure for our desired computation, which can be interpreted later. This also allows us to decouple the definition of a DSL from its interpretation. In this talk we explore the use of Free monads to easily define DSLs and their interpreters.