Programming languages are (generally) human-friendly means of getting a computer to do something interesting on a human's behalf. Some languages may be friendlier than others; where opinions will genuinely differ as to which are friendlier, there are some that deliberately seek to be as obtuse and unreadable as possible, and those are usually called "esoteric" programming languages and fit into their own category of madness.
"Every configuration file becomes a Turing-complete language eventually." --James Gosling
Every simple language will eventually end up Turing complete
"The 7 ur-languages": ALGOL, Lisp, ML, Self, Forth, APL, Prolog
ALGOL
Characteristics. Programs consist of sequences of assignments, conditionals, and loops, organized into functions. Many languages add module systems, ways of defining new data types, polymorphism, or alternate control flow constructs like exceptions or coroutines.
Examples. Most common programming languages trace to this ur-language. ALGOL itself included ALGOL 58, ALGOL 60, ALGOL W, and ALGOL 68. Assembly languages for mainstream processors, Fortran, C, C++, Python, Java, C#, Ruby, Pascal, JavaScript and Ada all trace to this ur-language.
History. This is the oldest ur-language, going back to Ada Lovelace formulating programs for Babbage’s analytical engine. The machine and assembly languages for all Eckert-Mauchly architecture computers, going back to EDVAC and the first Univacs were of this form, as were all early attempts at higher level languages, starting with Grace Hopper’s A-0 and going through Fortran and COBOL. In the 1960’s the academic computer science community developed structured programming to make programming in these languages more manageable, which led to ALGOL 60, which basically all members of the class derive from.
Over time, members of this family accrete features taken from other ur-languages. In the 1980’s, notions from the Self ur-language were grafted onto many of these language in the form of classes as a way to define data types and do polymorphism. Since 2010, ideas from the ML ur-language have been appearing.
Lisp
Characteristics. Lisp consists of prefix expressions enclosed in parentheses. This syntax seems bizarre, but the language also has a built-in representation of lists as a data structure as parentheses around the space separated items (e.g., (1 2 3 4)). Thus the code is in the form of a list, and Lisp systems let you define macros that take a list, modify it, and pass that modified code to the compiler.
Lisps tend to behave like some other ur-language when writing most code (usually ALGOL or ML), but are distinguished by the macro system that lets the programmer redefine the semantics of the language. Common Lisp, for example, has a loop syntax, but it is defined as a macro, not built into the language.
Examples. There were many early Lisps, but the community achieved a consensus in Common Lisp. Meanwhile, Sussman and Steele explored how much could be done with functions and produced Scheme. Several other special purpose Lisps such as Lush (for numerical computing), AutoLISP (the scripting language for AutoCAD), and Emacs Lisp (the language used to implement editing behavior in the Emacs editor) have been used. In recent years Clojure has emerged as a third major branch of the Lisp family.
History. Lisp is about a year younger than Fortran, which makes it the second oldest language still in use today. Its origins were in a purely mathematical question: how do you write down a mathematical structure you can define that can evaluate its own expressions? John McCarthy provided an answer in 1958, which then got implemented on a computer. That mathematical background made early Lisps awkward fits for the machines they were on. Questions about memory and CPU cycles were irrelevant to the mathematics, and things like garbage collection had to be invented to make it work.
There was a period in the late 1970’s and early 1980’s when machines were specially built to run Lisp from the ground up. Much of today’s integrated development environments was invented on those machines. Lisp itself was the vehicle of choice for most artificial intelligence research in that period, and when artifical intelligence’s hype in the 1980’s failed to deliver, the field, and Lisp with it, crashed into what is called the “AI Winter.” Lisps remain stubbornly alive to this day, especially as computers gained power and other languages adopted many of the features that originally made them awkward to implement.
ML (functional languages)
Characteristics. ML languages are defined by functions being first class values and a type system in the Hindley-Milner family that is adequate to represent different kinds of functions and tagged unions. All iteration is done by recursion, as in
sum : list of int -> int
sum [] = 0
sum (x:xs) = x + sum xs
or by defining functions that encapsulate the iteration pattern and take another function to implement the behavior.
map : ('a -> 'a) -> list of 'a -> list of 'a
map _ [] = []
map f (x:xs) = (f x) : (map f xs)
Some languages in the family (Miranda and Haskell) are lazy by default, that is, they do not evaluate anything until it is actually needed. Others explore extensions of the type system in various directions. OCaml attempts to merge notions from the Self ur-language. Agda and Idris mix values and types (what is called dependent type systems) and 1ML mixes modules and types.
Examples. ML spawned CaML (Cambridge ML), Standard ML, OCaml, and a whole related family such as Miranda, Haskell, and today the dependently typed languages like Agda and Idris.
History. ML was the metalanguage (thus the name) for a theorem proving program developed in Cambridge, England. The language escaped from that context and was popular in Europe, particularly in England and France.
Self (object-oriented languages)
Characteristics. A program consists of a set of objects that can receive and send messages to each other. All behavior is implemented in this way. You create a new object by sending a message to an existing object. You do conditionals by having a variable which refers to either the true object or the false object. Both take a message with two parameters, a function to run on true, and a function to run on false. The true object runs the first function. The false object runs the second. The calling code does not know which it is sending to, only that it is sending a message. Loops are the same. Indeed, by creating and inserting appropriate objects into the right places you can entirely redefine the semantics of the language.
These languages usually have their source stored in a live environment rather than text files. The programmer modifies the live system and saves its new state rather than compile files to produce a system.
Examples. The two important examples are Smalltalk and Self. A whole range of languages implement message passing to objects in some subset of the language. This kind of partial import is usually referred to as “object oriented programming.” Most of these are modeled on Smalltalk. JavaScript is the exception, and derives from Self’s classless object system.
The ideas were taken in two other important directions.
First, Common Lisp’s object system generalized the idea of choosing what code is run based on what object receives a message. It disconnects behavior from the objects and instead the runtime chooses which behavior to run based on all the parameters involved, not just one.
Second, Erlang switched the notion of a thread of execution jumping from object to object to run various code and instead had parallel threads of execution that explicitly listen for and send messages.
History. Smalltalk was the original language, developed at Xerox Parc in the late 1970’s and 1980’s. There were a variety of commercial Smalltalk systems in the 1980’s, and IBM used Smalltalk to develop its programming tools for other languages (the collection of tools known as VisualAge). Today Smalltalk largely survives as the open source Pharo Smalltalk.
A lot of work was done on how to make Smalltalk run fast and efficiently, culminating in the Strongtalk project. Strongtalk is historically important because its discoveries became the basis of the HotSpot just-in-time compiler for Java.
Smalltalk inherited the notion of a value and its type from earlier languages, and implemented the idea of a class. All objects had a class that gave their type, and the class was used to construct objects of that type. Self disposed of the notion of class and worked solely with objects. As this is a purer form, I have chosen Self as the type specimen for this ur-language.
Forth (stack languages)
Characteristics. Stack languages are an inverse of Lisp, and share the grammar of Hewlett Packard reverse Polish notation calculators. They have a data stack. When you write a literal like the number 42, it is pushed to the stack. When you write the name of a function, it takes no explicit parameters. Instead it operates on the stack. Simple arithmetic looks quite backwards
2 3 + 5 *
and function definitions are equally terse. In most Forth variants, : defines a new word, in this case square. When square is called it is the same as calling dup, which duplicates the top element of the stack, followed by *, which multiplies the top two elements.
: square dup * ;
3 square
Forth allows programmers to intercept the parser and replace it with their own code, so the grammar is entirely replaceable. It is common to see Forth programs that define small languages, such as a Fortran subset or a way to directly ASCII parse diagrams giving packet layouts or the transitions of state machines.
Examples. Forth in all its many variants, PostScript, Factor, Joy (a purely functional language that uses a mathematical formulation of composition in place of the stack).
History. Forth was originally written in 1970 to control radio telescopes, but then spread broadly in embedded systems. It is sufficiently easy to bootstrap a Forth system that there are dozens of variations created by different programmers for thir own purposes.
PostScript emerged in the 1980’s as a flexible means to describe documents to printers. It is much more limited in many ways than Forth, but defines primitives related to graphical layouts in the language.
APL (array languages)
Characteristics. Everything in the language is an (n dimensional) array. Operators are one or two symbols long, and implement high level operations over these arrays. The result is so terse that the sequences of symbols become the label for an operation rather than giving it another name. For example, to calculate the average of an array in variable x, you would write
(+⌿÷≢) x
Examples. APL, J, K. The higher order operations over arrays have been partially exported into many environments, such as MATLAB, NumPy, and R.
History. APL began as a mathematical notation created by Kenneth Iverson in the 1960’s. He then implemented it on a computer. It has enjoyed a niche following ever since among people doing heavy calculations. Its descendant, K, was very popular in financial settings.
Prolog (logic languages)
Characteristics. Programs consist of facts, either “ground” facts such as Bob is Ed and Jane’s father,
father(bob, ed).
father(bob, jane).
or non-ground facts which define how to derive a fact from other facts by putting in variables (which are capitalized)
grandfather(X, Y) :- father(X, Z), father(Z, Y).
Prolog runtimes take these facts and a query on them and searches for a result for the query. And it turns out that if you choose the right structure for defining facts, this is Turing complete.
The terms that form facts in Prolog are a native data type in their own right that can be created and then fed to the runtime, the same way that Lisp’s macros or Forth’s parser replacement work.
Because Prolog programs are basically searches, they are tuned rather the way database queries are, adjusting the order in which things are searched and cutting off paths that will not yield anything as early as possible.
Examples. Prolog, Mercury, Kanren. The vast majority of programming around this ur-language takes place in Prolog itself — the community is impressively unified.
History. In the 1970’s, logicians in France realized that they could express programs in terms of first order logic, and began trying to implement this. In the 1980’s the Japanese fifth generation computer project bet heavily on Prolog, and when that project failed, Prolog went down in reputation with it.
Meanwhile, decades of research continued into how to make Prolog runtimes smart enough to be efficient in most cases and how to add new capabilities, such as numerical constraints (yielding constraint logic programming).
Prolog tends to show up in niches. Type checking for Java was for many years implemented in Prolog, as was Facebook’s original source code search tool.
Multi-paradigm design: The idea that a languate can offer multiple paradigms, simultaneously.
Aspect-Oriented: Attempting to capture as first-class citizens the code that "cuts across the inheritance tree" of object-oriented languages.
Functional: Functions as first-class citizens; incorporating more maths-like concepts into the language.
Literate: Code as documentation as code.
Logic: Defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules.
Meta-object protocols (MOP): A series of ideas about how objects relate to one another through means other than inheritance trees.
Object-oriented: The union of state and behavior in a first-class construct.
Phi-Calculus: ...?
Quantum: Quantum programming is the practice of writing algorithms and applications for quantum computers or quantum simulators. It draws on quantum mechanical phenomena such as superposition, entanglement, and interference – to encode and process information in ways that classical computing cannot. A quantum program specifies a sequence of gate operations applied to qubits, measurement operations, and classical post-processing logic. Classical programming is deterministic – input goes in, output comes out. Quantum programming, by contrast, is probabilistic – algorithms must be designed to amplify correct answers through quantum interference while suppressing incorrect ones. The constraint that shapes everything else is measurement. Classical code can inspect any variable at any point. Quantum code can only extract information by measuring qubits – and measurement collapses the quantum state, destroying it. Quantum programmers cannot use print statements or step-through debuggers on quantum variables. Algorithms need to be mathematically verified before testing, with validation relying on statistical error rates rather than deterministic checks. Resource constraints are also more severe. Classical programs run on billions of transistors. Current quantum programs must fit within 50 to a few hundred qubits on production hardware, with strict limits on circuit depth before errors accumulate. This makes quantum programming closer in spirit to embedded systems or Field-programmable gate array (FPGA) design than to standard software engineering.
"Programming Paradigms for Dummies: What Every Programmer Should Know"; includes a chart of the major programming paradigms:
Declarative, imperative, functional, constraint, logic, relational, concurrent, dataflow, synchronous, mesage-passing, event-loop, object-oriented, stateful, shared-state
My ontology:
"Class"-based object languages: The more "traditional" OOP systems in which an object is forged out of a template/"cookie-cutter" expression known as a class. Typically these languages are compiled and strongly-type-checked, and if runtime inspection facilites (reflection) are available, they will be read-only. Objects obtained as part of the runtime inspection facilities are generally read-only, and often cached/singletonized/memoized.
"Prototype"-based object languages: Most popularized by ECMAScript, prototype-based object languages have long existed, going as far back as CLOS, Self, Smalltalk, and others. Here, inheritance is a runtime relationship understood by the runtime (an object is cloned from a prototype "class" object, and as part of that cloning process a well-known pointer/reference is established back to the "class" object for "class"-level properties/methods), and is often malleable. Most prototype-object languages are dynamically-typed and often weakly-typechecked, since the runtime doesn't have a priori knowledge of how the various relationships are set up after the program has begun execution.
Note that any language which supports records (4.1) and lexical function closures (4.2) can build objects "by hand", and even support inheritance by defining a runtime convention of a particularly-named-slot in a record to hold a reference to a prototype object. Given that functional languages often support both, this often leads to (a) functional-object hybrid languages with varying degrees of support for both, or (b) functional language zealots to insist that objects are unnecessary.
Most object languages are imperative when defining methods, though this is not a requirement.
Functional: Characterized by mathematics-inspired functions as first-class concepts. Preference for expressions over statements, lazy execution, immutable values, and so on. | Wikipedia | Some reading | Functional programming brought us monads and all the fun that goes with that. Frequently cites/builds off of the lambda calculus.
"Programming Language Explorations" suggested tags: declarative, imperative, applicative, functional, logic, von Neumann, object-oriented, expression-oriented, persistent, concurrent, reactive, dataflow, stack, free-form, curly brace, visual, compiled, interpreted, very high-level, system, scripting, glue, intermediate, esoteric, toy, educational, domain-specific
Languages often break down to automatically-managed memory vs. manually-managed, but most of the time that means heap management; static and stack allocation are (almost?) always automatically managed by virtue of their allocation schemes.
There's also relationships to physical memory that merit examination.
Compiled: Source is ahead-of-time transformed into a binary output ready for "direct" execution--it can be loaded and run without additional help. Most compilers do some form of validity-checking, usually making use of type-checking, to ensure the program will run correctly. Several "flavors" of compiled targets fall into different categories:
Native: Source is compiled into binary format ready for direct execution by an operating system.
Transpiled: Source is ahead-of-time transformed into another source format. Term became popular with languages that transpiled into ECMAScript, but technically this has been around since long before then. (When I was taking ECS 140A at UCDavis in 1991, we had an assignment to write a language that transpiled into C, for example.) C has long been the preferred target of transpilers before ECMAScript, given its original goal of being a "higher-level assembly language". C-- is a variant of C made specifically for transpilation/compilation purposes.
Bytecode: This is usually a binary output format that is some level of abstraction higher than native CPU assembly language, but not much more. The intent is to provide an easier format for either direct execution or transformation into native assembly (Just-In-Time compilation) within a virtual machine. Some bytecode formats are well-documented and "external" (like the JVM and CLR formats). In many cases, however, an interpreter is actually doing an on-the-fly compilation of source to an internal bytecode format at runtime, since most bytecode formats are easier to JIT, and/or the bytecode provides a layer of abstraction from the language syntax to the underlying runtime.
Interpreted: Generally this means that source is not pre-examined until the moment it is asked to run. At that point, validity-checking is done, whether "just" syntactical verification or an additional deeper type-based analysis. Often after interpretation it is stored in a more terse/compressed/accessible form, like a tree or even an intermediate representation or bytecode form for easier execution; sometimes it is compiled to binary/assembly for direct execution.
Note that the lines are significantly blurring; several "interpreted" languages internally compile the source into an AST or bytecode format for faster execution, and several "compiled" languages are available to run at the command-line a la shell scripts or REPLs by internally taking source through their compilation pipeline and executing the result.
In the long run, the key differentiator around "compiled vs interpreted" may be solely whether the tool/language does ahead-of-time error-checking.
A type system (a system in which one can define and/or declare types) is the mechanism by which the language detects errors either ahead of time (during compilation) or at runtime.
Types can often be inspected at runtime regardless of the type-safe or type-checked nature of the language/platform; in many languages/platforms, the process of inspection is known as "reflection".
If the types can be manipulated/changed at runtime, then the language is often said to be a "MetaObject Protocol" language, meaning that it can operate at a "meta-object" level. These tend to be dynamically-type-checked/weakly-type-safe interpreted languages, since it is hard to enforce type-safety when types changing up at runtime.
Last modified 01 September 2026