Friday, 10 January 2014

Why Functional Programming?

I was in attendance at FPDays2013 to hear Bodil Stokke's talk: Programming, Only Better. I revisited this talk recently in order to put together the case for functional programming and F# to my colleagues. The first part of my presentation is largely taken from Bodil's talk, so this is basically a transcript with a couple of points from different sources. Primarily Scott Wlaschin's F Sharp for Fun and Profit.


Programming Techniques

We strive to make it simple to create programs. We use technology, tools and techniques to make programs better and to make better quality programs. The most basic tool we have is our own reasoning. Other techniques include testing, pair programming & code reviews. We'll explore 2 of things.

Reasoning - We reason about code when we are trying to infer what it will do. We reason to better understand what is going on. Debugging is the most tangible form of reasoning. Typically, we are following the code path and understanding the flow of data. In every program we write we have to reason about what the code is doing. In its most basic form, we are walking through the program in our minds.

Testing - while we can be sure that programs are always reasoned about - they are certainly not always tested. That is because testing is useful, but limited. Testing can only show you the presence of bugs, but never their absence. TDD is based around this fact. The Red; Green; Refactor; process highlights the presence of a bug as the code is not yet written to perform this task - so the test fails. Writing the code to make the test pass is the process of removing a bug. Just because a system has 100% code coverage it cannot be said to be 'bug free'.


Programming Difficulties

Programming is difficult largely because programs grow to be complex. Code volume also makes it difficult to maintain programs, but volume and complexity are not necessarily linked. Its very easy to create an overly complex program without applying thought to its design. Simplicity is much harder to achieve. We'll look at one source of complexity:

State - object oriented languages idealise encapsulated state. OO languages are useful precisely because we use more code to manipulate an object's state. So state changes with time - known as entropy in the mathematical world. Most of the time, we are intentially changing state in a useful way. But state is also permitted to be changed by something outside our control (unless we are very careful). The code example below highlights what it means to have an object enter a 'bad state' and not have that communicated back to the calling code.


State Spoils Testing - how can you test an object in all its possible states? You can only test for the states you think the object might enter into. State increases exponentially, so the more potential state you create, the lower the meaning of your tests!

State Spoils Reasoning - state allows for unpredictability. You can never be sure that you will get the same output with the same input. An objects state can change, so how can you be sure of it at any given time? This becomes even worse when you consider concurrent programs. It is much harder to describe the control flow of a program concurrent stateful program. In OO you have to use Locks to be able to protect against unwanted state change. This is cumbersome, inefficient and still imperfect.


Functional Languages

What can they do to help? They idealise Referential Transparency. RT means dealing with inputs and outputs. A function just maps an input to an output.

Predictability - is guaranteed in a functional language because you will always get the same output for the same input. It can be thought of as nothing more than a glorified switch statement!

No Side Effects - as there is not state! There are no variables in pure functional languages, only values. Once a value is created, it cannot be change. Values can be created local to functions, but that function is also immutable and cannot be changed. State is instead managed by passing values around as inputs and outputs. When you think about it, it doesn't make sense to want to change a value you have set. What makes sense is to simply set a new value. This is essentially what functions do.


State

What happens when we have no state? Reasoning becomes much simpler! Predictability makes programs easier to understand. Tests also becomes more meaningful for the same reason. However, your tests are still unable to tell you that bugs do not exist, and they are only meaningful for the inputs you are testing with.

Optimizing - once you realise that output will never change for a given input, you understand that it no longer matters what order functions are called in. This allows you to perform functions in parallel, such as map reduce. Lazy Evaluation also becomes possible, because you know it doesn't matter when you call a function - the result will always be the same for the give input. Caching is more widely permitted for the same reason. Also, as there are no side effects, it means that you can be sure nothing is going be changed from underneath you! This makes concurrent programs easier to manage, as you don't need to worry about race conditions and lock objects.


Conclusion

The techniques we use to help delivery quality software are undermined by state. Functional Languages remove state from the programming paradigm and in doing so prevent an entire class of bugs from ever entering our code. Functional programming makes our code easier to understand because of its predictability and has greater potential to optimize.


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