Late Binding in Programming Languages

Late binding is key to enhanced productivity in programming languages. I believe that this is the single most important reason why so-called dynamic typed languages are so popular.

This note is part of an ongoing ‘language design’ series which aims to look at some key aspects of programming language design.

What do we mean by late binding?

Simply put, a programmer should not have to say more than they mean at any particular time.

To see what I mean, consider a function that computes a person’s name from a first and last name. In Star, I can write this:

fullName(P) is P.firstName()++P.lastName()

This constitutes a complete definition of the function: there is no need to declare types; furthermore this function will work with any type that has a first and last name.

Contract this with a typical well-crafted Java solution:

boolean fullName(Person P){
  return P.firstName()+P.lastName();
}

Not so different one might argue.

Except that we have had to define a type Person; at best this is an interface and not a class. The Java fullName method will only work with objects of type Person.

In designing the Java version we have to find and/or create a type Person. In addition, we must make sure that all classes that we want to compute the full name of implement the Person type.

This last aspect can be a real productivity killer. Suppose that we want to be able to compute the full name of many different kinds of objects; then we must arrange them all to implement the Person type. This may not even be possible if the classes in question are from a library that you don’t have the source to.

Late Binding does not mean dynamic typing

One of the common perceptions is that you lose the safety of a type checker if you want to allow late binding. This is not true; at least, it is not true for Star.

Star is a statically typed language which supports a range of type constraints. In the case of the fullName function, the constraint is that the type of P has the firstName and lastName attributes. (The details of how this is done are too gory to go into here.)

These constraints can be checked; so, for example, every time the fullName function is used on an argument, the checker can verify that the type of the argument is consistent with having a first and last name. This check can be performed at compile-time.

It is even possible (necessary) to go one step further and allow generic functions to use the fullName function.

Other forms of Late Binding

Late binding shows up in other ways. For example, when specifying an imported library of some form or other, it should be possible to declare the requirement for a library in terms of what is needed, rather than the name of the library. I.e., instead of:

import java.util.List;
import java.util.ArrayList;

we should be able to say:

require List of integer;

or some such expression.

The difference is that the former says — in the source code — which package to import whereas the latter merely declares a contract requirement. How the contract is fulfilled is a separate step; one that a smart compiler system may even be able to automate.

(Some language do work this way. Typically LISP language systems organize their modules in terms of requires and provides.)

Take Away

It can be hard to know what features should go into a programming language. Having a few principles to guide us make the task of designing a language more tractable.

In this case, the message is that programmers should be able to program in terms of their requirements, not the compiler’s. This need not come at the expense of safety or of performance.

Productivity gains are permanent, Performance losses are temporary

This is the first in a short series of notes about language design. The goal is to identify what is truly important about programming language design.

It is all about productivity

There are ‘too many moving parts’ in a typical Java program.

Here is a simple straw-man example: iterating over a collection. Before Java 5, the paradigmatic way of iterating over a collection was to use a fragment like:

for(Iterator it=coll.iterator();it.hasNext();){
  SomeType el = (SomeType)it.next();
  ...
}

There is a lot of ‘clutter’ here; but worse, there are a lot of concepts that are not really that important to the problem at hand.

Compare that to the Java 5 version:

for(SomeType el:coll){
 ...
}

Here we can focus on exactly what is needed: the collection and the iteration.

This kind of example can be repeated fractally up the entire stack of abstractions: not just at this micro level but also at the level of using standard JDK class libraries on through to using features such as Spring/Hibernate or Wicket.

The elements of productivity

I believe that the list of requirements is not that long:

Digestibility

A programming language is an artifact, and needs to be understood in order to be effectively used. So, a language with a well crafted structure is going to be easier to learn and use than one with many ill fitting pieces.

Note that this does not mean a small number of pieces (or features). The classic example of this is natural languages (such as English or Spanish). There are at least 500,000 words in the English language. A daunting task for someone wishing to learn the language. But, the grammar of English is relatively simple compared to other languages (such as German or modern Greek). This has helped English to become the dominant second-language globally.

Semantic Lifting

Structure in software is evident at many levels: in the micro structure of individual fragments of code, to the organization of libraries to the ecosystem of multiple applications.

A powerful tool for managing this complexity is abstraction. Commonly abstraction is thought of as a technique that enables one to ignore inessential details.

But a better concept might be semantic lifting. Semantic lifting is a common technique in Mathematics. For example, vector geometry is layered over cartesian geometry but permits powerful statements to be expressed in a simple way.

A programming language that can support similar techniques for lifting the language — like the iteration example — enables higher productivity.

Late Binding

Programming languages are often quite fussy in character: requiring all kinds of details to be established quite early in the design process. For example, Java (like most languages) requires that all types have names.

This emphasis on detail makes a significant barrier to developing a program: the programmer is forced to focus on issues that she may not actually be ready or willing to.

A language that supports late binding allows programmers to delay such choices until they are needed.

For example, one reason that type inference is so powerful is that it permits a programmer to program in a ‘type-free’ way while knowing that the compiler will verify the type safety of the program. Establishing types of functions is a detail that can often be left to later. However, ultimately, type inference still ensures that the program is ‘correct’.

Declarative Semantics

This one is a hard one!

But the fundamental benefits of a declarative semantics arise from the tractability that follows. Having a declarative semantics makes program manipulation of all kinds more straightforward. That, in turn, means that some high-powered transformations — such as those required for scaling on parallel hardware — much easier to accomplish without requiring enormous input from the programmer.

What does the future language look like?

According to this thesis, a language based on these principles would have a sound semantic foundation, would be easy to understand and would not require more from the programmer than was required by the problem. And it would be easy to deploy on systems consisting of many cores.

What’s not to like? In the subsequent posts I will look at each of these principles in turn in a little greater depth.