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Lambda World 2018 - Diagrammatic Execution Models for Functional Languages - Keynote

Program Representation Graphs PRGs are used by Yang "A program Integration Algorithm that accomodates semantics-preserving transformations", in a program-integration algorithm to facilitate the semanti level program comparison. Dead Code Removal removes dead statements identified in a program from the AST representation of the program.

Reasoning About Program Transformations

If a temporary variable is not used in the program, the declaration of the variable is also removed. The outputs are: a equivalent map, tuextual difference map, and an unmatched set. Detect errors: the compsarison results are processed further in a error detection step. The SP statements in the equivalent map and the textual difference map are recognized as correct statements. For every unmatched SP statement, the most similar unmatched MP statement is found, and the differences between the two statements are identified in SP.

Among these differences, those that are actually legal variations of SP are leanred and eliminated by the system. Unresolved differences are reported as errors in the diagnosis report. By now you realize why pure functions are the pillar of FP, but we have one more concept to introduce. If pure functions are the pillars, then composition is the base or the glue that makes up the entire system. At the core, functional programming is effectively the interplay between decomposition breaking programs into small pieces and composition glueing the pieces back together.

It is this duality that makes functional programs modular and so effective. Thinking functionally typically begins with decomposition or learning to break up a particular task into logical subtasks functions. Modularization in functional programming is closely related to the Singularity principle one of the SOLID design principles , which states that functions should have a single purpose.

The idea is that you learn to decompose programs into the simplest reusable units of composition.

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In FP, your functions should abide by this motto:. Purity and referential transparency will encourage you to think this way because in order to glue simple functions together they must agree on their types of inputs and outputs as well as arity or number of arguments. In short, they must agree on their exposed contracts, which is very similar to the Coding to Interfaces OO design principle. A practical observation and not a formal concept indicates that the lower the number of function parameters, the simpler the function tends to be. This process is essentially what compose does. This is essentially the same coding to interfaces but at a much lower level of granularity.

This will set the stage for developing data flows in chapter 3. So stay tuned! I mentioned in chapter 1 that functional programming is not a new framework, library, or design pattern. However, paradigms by themselves are just abstract concepts that need the right host language to become a reality. And, yes! PHP is that language. The goal of this chapter is to teach you how to use functions in a very different way—the functional way.

A higher-order function is defined as one that can accept other functions as arguments or return another function. Both are intimately related, as the ability of a language artifact to be passed in as an argument or returned from a functions hinges on it being considered just another object. This also means, of course, that functions can be assigned to variables. Functions in PHP can be manipulated just like objects. This means that you should be able to treat a function just like any other type of object.

Which means that they can be assigned to variables.

How to wrap your imperative brain around functional reactive programming in RxJava

Consider a simple string concatenation function:. Functions can also be returned from other functions. This is an extremely useful technique for creating families of functions. Consider a simple concatWith function:. Supplying functions as parameters allows you to administer specialized behavior on top of another function. Suppose I create a simple function that takes a callable and applies it over its other parameters:.

I mentioned earlier that higher-order functions allow you to supply specialized behavior via function arguments. Correct, a division by zero error:.


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This approach avoids throwing an exception altogether. In particular, it fails to obey spatial locality , which states that related statements that should be executed sequentially shall be placed near each other. This has more application on CPU architecture, but can also be applied to code design.

Data-flow testing of declarative programs

In these cases, your best bet is to wrap them using your own. Before this version, this was just considered an internal design decision, but now you can reliably take advantage of it to approach problems very differently. Furthermore, because functions are true instances, as of PHP 5. Aside from having true first-class, higher-order functions, PHP takes it to the next level with invocable objects. To put it another way, doing away with the use of this. Consider this simple Counter class:. So, from a practical standpoint, this object is predictable and constant. In practical functional programming, there are many design patterns that revolve around wrapping values into objects and using functions to manipulate them.

One way of doing this to keep things semantically meaningful is to prefer static functions that declare arguments for any data they need to carry out their job:. However, we can do better. We can use containers to do this. We cans use wrappers to control access to certain variables and provide additional behavior. Looking at it from an OOP design patterns point of view, you can compare this to a Fluent Object pattern. To start with, consider this simple Container class:.

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Containers wrap data and provide a mechanism to transform it in a controlled manner via a mapping function. For instance, I can apply a series of transformations on a string fluently like this:. Notice how this looks much cleaner and easier to parse against nesting these function calls one inside the other.


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Personally, I rather see code in a flattened out and linear model than something like:. Consider the implementation for a SafeContainer :. It looks very simple, but the effect of this wrapper type is incredible; most things in functional programming are very basic but with tremendous impact and reach. One thing I like about this function is how honest it is about its return type.

Also, it removes the burden of having to NAN-check any functions that are invoked with this value. In chapter 1, I briefly mentioned that behind the scenes, all functions are instances of the Closure class. This is what enables me to map them to containers.