git_prompt: Type driven development in Rust

This post is from a series of posts about writing a small application in Rust to display information about a particular git repository.

In this part I try to explore the problem by relying on the Rust's compiler and the sum types.

Rewriting, the type-driven way.

I am really happy with what I have learned during this exploration of the rust language and I think that it is has been a really nice journey. However, there was one more thing which I wanted to try out, which I did not manage to do with the previous version of the CLI.

Type driven development is only really possible when there are no NULL values and you can explore how the types/possible failure states constrain the high level structure of the domain logic of your program. With functional languages like F# or Haskell you can leverage the fact that the types can be inferred automatically, but with Rust one can start testing out how to work with the following developing flow:

  • Investigate the structure via the type system (e.g. Result, Option).
  • Implement the minimum happy paths.
  • Stabilize the implementations by adding unit tests.

I have recently started working with go in my day-to-day programming at work and I realized that one could also start reusing concepts like:

  • Accept interfaces, return structs.
  • Use the trait system to make external structs implement the locally defined traits.

All of the things mentioned above means that I could write the whole program in a slightly different manner than before. You can find the code on github.

Type driven

What I like the most about rust is the sum-types and how they change the way one needs to think about the domain. The fact that you get Option<T> or Result<T, E> from a particular function means that you will need to do some error handling or potentially think about how to recover at the place where the error occurs if you do not want to propagate the Result type up the stack. The similar is true for go as well, which helps one to write relatively stable code early on.

However, where this really shines is when you start modelling your program as a motion of data. At the end of the day the whole CLI design is all about the data. This means that researching the domain of the problem with data structures where one passes them around is a reasonably natural thing to do. This has the following implications:

  • One can decouple the software from the parts of the code which interact with the outside world.

  • One can more easily bundle the data that needs to be moved together, which makes the interaction of different code pieces much easier.


There were some interesting discoveries along the way and the most important realisation was the fact that the whole application becomes much easier to test the functional Model-View architecture is being used. Since this is not a long-running application, there is no need to think how the application is going to be updated, but the potential for generalisation is there.

Testing was another thing that surprised me. I have a lot of scientific/engineering related background and the code that one writes there most of the times deals with data transformations involving complex algorithms and mathematical formulae. The data input into the system most of the times constitutes only a small part of the system.

However, when one deals with querying of stateful systems and the part that matters is the representation of the data in those stateful systems, the hard part becomes either the UI, or the queries of the stateful systems and not as much the data transformations. This means that creating a seam early on to not rely on mocks to test the system is really good.

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