# Programming Language Concepts¶

Note

These slides are also available in PDF format: 4:3 PDF, 16:9 PDF, 16:10 PDF.

## Learning Group Activity¶

1. Share your code snippets from the assignment. Explain why one language is inherently less maintainable, readable, or abstractable in one language than the other for that particular example.
2. Collectively, as a group, either:
1. create a great definition for expressivity
2. or, create a great explanation for how expressiveness differs from (and is similar to) conciseness

Prove Them Wrong?

Think that there’s a better way to express the problem for a piece of code your group member is showing? Show an example.

Remember to be nice.

## Language Implementation Techniques¶

### Compiled Languages¶

• Runtime is fast!

• Compile time is slow
• Source code cannot be a part of the input data

Examples

C, C++, and FORTRAN are generally implemented as compiled languages

### Interpreted Languages¶

• No need to compile
• Source code can be a part of input data: you can transmit functions across the network to be run!

• Runtime is slow

Examples

BASIC, PHP, and Perl are generally implemented as interpreted languages

### Hybrid Interpreters¶

To speed up the execution of interpreted languages, implementers started getting clever:

• Interpreted VM Bytecode: Input is lexed, parsed, then translated to bytecode. The bytecode gets optimized, then the low level bytecode is interpreted. Examples: Python, Java, Ruby
• Just In Time Compiler: Source code is compiled as it’s executed, putting machine code on the processor “just in time”. Examples: PyPy, LuaJIT, Chrome V8

Advantages include all the benefits of interpreted languages, with run times occasionally approaching compiled languages.

## Evaluating a Programming Language¶

### Evaluation Metrics¶

Evaluating programming languages based on:

Writability: How easy is it to write good code? How easy is it to read well written code? Is the language easy enough to learn? What features does the language provide to make sure our code works as it is supposed to? Does an interpreter or compiler actually exist for the platform we need to use? Is it fast enough for our application?

Often times, adding features which improve one metric can harm another metric. Examples to come…

### Simplicity¶

The overall simplicity of a language plays a large role in both writability and readability.

For example, these features are non-simple:

• Feature Multiplicity: 👍 Writability, 👎 Readability
• Large Grammars: 👍 Writability, 👎 Readability

Simplicity can be carried too far

Assembly languages and esoteric languages generally aren’t considered very writable or readable.

### Orthogonality¶

Orthogonality: how consistent is the language with itself?

Example of a lack of orthogonality (C++)

Parameters are passed by value, unless they were specified with an &.

Or unless they were an array.

Example of a lack of orthogonality (C/C++)

Arrays can contain data of any type, including pointers.

Unless it’s a function pointer.

But you can wrap that function pointer in a struct and you should be fine.

Impacts of poor orthogonality: poor readability, poor writability, and potentially reduced reliability.

### Abstraction¶

Abstraction: The ability to define and use complicated structures and operations in a way that allows implementation to be ignored.

Examples:

• Functions: Simplest form of abstraction. Often taken for granted, but gives us easy recursion.
• Heap Memory: Imagine trying to create a large unbalanced binary tree in a single-dimensional array.
• Generics: Allows us to define operations that apply to multiple data types without reimplementing for each type.
• Garbage Collection: A form of automatic memory management.

What other kinds of PL-level abstractions can you name?

Good Abstractions: 👍 Writability, 👍 Readability, 👍 Reliability

### Reliability Features¶

Some languages come with features designed for reliablitiy:

• Type Checking: Making sure the type of data can be used with the function or operation you are calling. Independent of static/dynamic: more on this later.
• Exception Handling: The ability of a running program to intercept run-time errors and take corrective measures.
• Taint Protection: Protects the security of an application by not allowing privileged operations to be preformed on tainted data (e.g., user input from a web application).

Some features can harm a language’s reliability:

• Goto: the ability to jump to different locations in the code without restriction.
• Aliasing: allows two different symbolic names (variables, function names, etc.) to refer to the same data. Think pointers in C/C++.

### Expressivitiy¶

1. If one language is less expressive than another, how might it be less writable?
2. If one language is less expressive than another, how might it be less readable?
3. If one language is less expressive than another, how might it be less reliable?

## Typing Systems¶

### Bindings¶

A binding refers to the association between:

• a variable and its type,
• a function and its definition,
• a type and its representation (e.g., int is 32-bits),
• or an operation and its symbol (e.g., multiplication is usually *)

Binding time refers to the time at which a binding takes place.

Common Binding Times

Design time, implementation time, compile time, link time, run time

### Static Typing¶

In a static typing system, the binding of a variable to its type occurs before run time.

In other words, the type of data is associated with the variable.

int x = 12;


• No need to do type checking at run time, this can be done at compile time.
• 👍 Reliability

• Generics are needed to create operations and functions that apply to multiple types
• 👎 Writability

### Dynamic Typing¶

In a dynamic typing system, the binding of a variable to a type occurs during run time.

In other words, the type of data is associated with the data itself.

x = int(12)


• Collections can be of mixed type without generics, functions can take multiple types without generics
• Types can be dynamically created at run time
• 👍 Writability

• Type checking must be done at run time; makes things slow
• 👎 Reliability

### Untyped Systems¶

In an untyped system, variables are never bound to a type.

In other words, the functions and operations called on the variables determine the type:

12 x define
x int->string print-string


Note

Don’t confuse untyped for type inference. Type inference is generally used with static typing systems.

• No need to do type checking, ever.
• 👍 Feasibility

• 👎 👎 👎 Reliability

### Strong and Weakly Typed¶

• Type safety means a language will not allow bits to be intepreted as the incorrect data type. For example: treating the bits of an integer as a floating point number.
• Implicit type conversions are when a language will automatically convert data types to allow an expression to be computed.
• Strongly typed programming languages are both type safe and do not allow implicit type conversions.
• Weakly typed programming languages are either not type safe or allow implicit type conversions.
• Whether a language is strongly or weakly typed has nothing to do with whether it is statically/dynamically typed, or compiled/interpreted.

### Type Systems: Language Examples¶

 Strong Weak Static Java, Haskell, Rust, Go C, C++ Dynamic Python, Ruby PHP, JavaScript