Using a programming language that supports first class functional programming doesn’t necessarily imply that the code you write is functional. Functional programming has the nice properties of being compositional - you design smaller abstractions and then reuse them to build larger ones. You use the power of algebraic properties that these abstractions offer to build larger elements of your model out of smaller ones.

 However often we find that many code bases designed in a functional programming language are inherently imperative, especially when you are using a multi-paradigm language like Scala or Java. There’s nothing wrong with imperative code, but it lacks the niceties of expression based composition that makes reasoning about your code so much easier. Also assignments used in imperative code are inherent side-effects that are best avoided (or at least minimized) in an expression oriented language.

 In this session I will discuss some of my experiences of mining imperative code and refactoring them into functional patterns. You will be surprised to see how much functional goodness can be derived through functional thinking. Algebraic structures like Monoids, Monads and Kleislis are just buried within the reams of imperative statements crying to come out. In an exercise of half an hour we will liberate them and see how the code quality improves from the point of view of modularity, compositionality and simplicity.

Domain modeling is usually implemented using OO design. In this approach of "nouns-first" we design object hierarchies using subtyping to model the various elements of the domain. In this talk we will take a different approach using Scala:

1. Start with focus on verbs first
2. Think how we can model domain behaviors using pure functions grouped within extensible modules
3. Use an algebraic approach towards evolution of modules,the algebra being composed of functions,types & laws
4. See how the domain algebra can be constructed from categorical structures like monoids, functors & monads
5. Discuss how the functional patterns lead to compositionality - an extremely important quality that scales your domain model.
   
   

Manual testing is something that's error prone, incomplete and impossible to replicate on a large scale. We have instead been using xUnit style of testing for quite some time now. This approach has a number of drawbacks like (a) We need to write test cases by hand which again doesn't scale for large systems (b) We may miss out some of the edge cases (c) Safeguarding missing cases with coverage metrics doesn't help, since metrics are mostly based on heuristics (d) maintaining test cases and test data is a real pain.

In property based testing we write properties and not low level test cases. And let the system generate test cases which validate such properties. There are 2 main advantages with this approach:

1. You think in terms of properties (or specifications) of the domain model which is the right granularity to think about
2. You don't need to manage test cases, which is completely done by the system that generates a large collection of test data

This approach is ideal for the functional programming paradigm, which focuses on pure functions. Using functional programming it's easier to reason about your model - hence it's easier to test functional programs using properties. In this talk I will take some real world examples of property validation and verification using scalacheck (the property based testing library for Scala) and a real world domain model.