The earliest programming languages – while archaic by today‘s standards – pioneered concepts and capabilities that influenced many of the advanced languages used now for developing software across business, scientific computing, mobile apps, embedded devices, and beyond.
By studying the history of these languages, we not only pay homage to the computing visionaries behind them but also better understand the gradual progression of key features, the expanding abstraction from hardware, standardization of syntax, and specialization across domains that evolved programming to where it is today.
The Need for Programming Languages
Early programming was done in cryptic machine code or assembly language, directly issuing CPU instructions as bits and registers. But as computer programs grew in complexity – especially for scientific and business applications – this low-level approach became unscalable.
New ways were needed to develop software at higher levels of abstraction – away from physical hardware details and closer to problem domains. The earliest high-level languages brought innovations like:
- English language syntax to aid readability
- Math notation translation to CPUs for scientific computing
- Business-specific features like report generators
- Portability across hardware platforms
Over time capabilities expanded from merely translating math formulas and file handling to complex data structures, recursion algorithms, dynamic memory, interfaces, libraries and more.
These pioneering languages – while esoteric today – laid the building blocks. Their influences cascaded for decades across subsequent languages that now dominate software development.
Timeline of Early Programming Languages
| Year | Language | Developed By | Key Contributions |
|---|---|---|---|
| 1947 | Assembly Language | Kathleen Booth | Mnemonic opcodes, machine abstraction |
| 1949 | Short Code | J. Mauchly, W.F. Schmitt | Auto-conversion of math expressions |
| 1952 | Autocode | Alick Glennie | Intermediate file compilation |
| 1959 | FLOW-MATIC | Grace Hopper | English syntax, business-orientation |
| 1956 | IPL | Allen Newell, Cliff Shaw, Herbert Simon | Lists, recursion algorithms |
| 1957 | FORTRAN | John Backus, IBM | Math notation translation |
| 1957 | COMTRAN | Bob Bemer, IBM | Picture clauses for data formats |
| 1958 | ALGOL 58 | Multiple committees, researchers | Structured programming concepts |
Next we‘ll highlight 8 pivotal languages – their purpose, innovations, and influence on programming languages that followed…
1. Assembly Language – Low-Level yet Mnemonic
The very first programming instructions were issued in absolute machine code – streams of 1‘s and 0‘s to manipulate CPU registers and memory. Reading and writing long code sequences this way was tedious and error-prone.
In 1947, mathematician Kathleen Booth conceptualized a more user-friendly method called Assembly Language programming. While still operating at hardware levels, Assembly used textual mnemonics – short, easy-to-remember keywords representing internal CPU instructions.
For example, 0000010 in machine code became ADD for adding numbers in Assembly. Other instructions like MOV, STA, COMP stood for move, store, compare operations. Instead of memorizing bit patterns, programmers could now use these intuitive abbreviated commands.
This concept was adopted in 1949 when the Electronic Delay Storage Automatic Calculator began using a Mnemonic Code variation for programming its arithmetic, logical, and control operations.
Today Assembly languages are still used where direct hardware control is needed – such as operating system kernels, drivers, embedded systems. But the principles of mnemonic opcodes, symbolic representation, and abstracting away raw machine code laid the foundation for more advanced languages.
Fun Fact: Grace Hopper helped popularize using English words for opcodes on the EDVAC machine in 1952. Instructions like ADD became ADD instead of A for accessibility.
2. Short Code – Automating Expression Translation
A major pain point working directly with early computers was converting complex mathematic expressions into machine addresses and control code. This manual process was tedious and error-prone.
To ease this burden, in 1949 John Mauchly, William Schmitt, and collaborators devised Short Code – initially proposed as Brief Code by Mauchly. Instead of requiring the programmer to calculate absolute hardware addresses, Short Code automated translation of arithmetic expressions into pure machine code internally.
For example, the programmers could represent mathematical computations and relationships symbolically using standard math notation, while the Short Code compiler handled rendering to low-level bit patterns behind the scenes.
Short Code was among the first high-level languages – abstracting away hardware details while bringing domain-specific notation for math and science applications. These early compilers also enabled optimization like parallel memory access.
The approach found adoption on pioneering computers like the UNIVersal Automatic Computer I (UNIVAC I) built by Eckert & Mauchly Computer Corporation. Short Code set precedent for languages intermediated by compilers rather than directly issuing machine instructions. This gave programmers greater capacity to represent real-world problems instead of endlessly crunching bits.
3. Autocode – The First Step Toward Compiled Languages
A watershed moment arrived out of Britain‘s University of Manchester where researchers sought easier ways to program the Manchester Mark 1 experimental computer.
There in 1952, Alick Glennie developed the Autocode system for Mark 1. Autocode introduced a critical innovation – an intermediate file representation. Rather than programmers writing machine code themselves, source code could be compiled automatically into intermediate language, then assembled into instructions executable by the hardware.
Autocode also provided various features to ease arithmetic expression translation – influenced by earlier work on Short Code. Considered the first compiled language, Autocode pioneered programming techniques soon adopted more broadly:
- Flow control – conditional IF statements
- Declaring variables
- Supplied math libraries for common calculations
- Abstraction from hardware specifics
The automated compilation process brought programming a level higher – no longer requiring intimate hardware skills to write software. These concepts would cascade across domains like business and research programming over the ensuing decades.
4. FLOW-MATIC – Pioneering Business Languages
A true pioneer across computing history, Grace Hopper led development of the FLOW-MATIC language beginning 1955. Released for the UNIVAC I and II systems by 1959, FLOW-MATIC focused business data processing.
Key innovations included the use of English language keywords, data-oriented features, self-documenting code syntax, and standard conveniences like automatic indentation. FLOW-MATIC also provided strong report generation capabilities – formatting program output into business transaction reports.
The most notable aspect was FLOW-MATIC‘s influence soon after on the COBOL language specification – released 1960 then standardized by ANSI in 1968. COBOL grew to dominate business application development over subsequent decades. Many FLOW-MATIC features lived on in COBOL:
- English language syntax –
COMPUTE,DISPLAY, etc. - Data Division concept with file SECTIONs
- Imperative statement structure – ADD X TO Y
- Comment delineation with
*marks - Code formatting like indentation
Now over 60 years later, COBOL still drives an estimated 80 billion lines of code across legacy business systems at large corporations, government agencies, etc. This spans 46% of critical applications – truly a testament to Grace Hopper‘s pioneering vision designing FLOW-MATIC!
Fun fact: Hopper also led the standardization of COBOL across early computer systems while serving as director of the Navy Programming Languages Group. Talk about influence!
5. IPL – List Processing and Recursion
In 1956, Allen Newell, Cliff Shaw, and Herbert Simon of Carnegie Mellon University developed the IPL language – IPL standing for Information Processing Language. One key intention behind IPL was facilitating artificial intelligence research – at the forefront whichSimon and Newell.
Designed for list processing and recursive functions, IPL introduced innovations like:
- Dynamic memory allocation (
GETLIST,FREELISTinstructions) - Data types – integers, booleans, lists
- Recursion – functions calling themselves
- Lists with related data bundled together
- Stack model for memory management
Lists provided an easy way to store variable sets of related data – no need to predefine sizes as with arrays. Recursion enabled new approaches to algorithms and problem solving by allowing functions to call themselves recursively.
IPL pioneered these concepts which became ubiquitous across many future languages. While not very widespread itself, IPL greatly informed LISP (1960) – considered ideal for AI with list/recursion strengths. These data structures even form the core of modern languages like Python, JavaScript, and more!
Fun Fact: LISP co-creator John McCarthy credited IPL‘s pioneering recursion as a key inspiration enabling AI problem-solving algorithms.
6. FORTRAN – Optimization for Scientific Computing
By the mid-1950s, the appetite grew for more specialized programming languages – mostly numeric and scientific computing using room-sized mainframes.
Seeking blazing fast math translation, in 1957 IBM researcher John Backus led development of FORTRAN – the name standing for FORmula TRANslation. FORTRAN improved upon earlier math notation in languages like Short Code, adding extensive features to optimize equation translation and execution efficiency – crucial when mainframe time was precious. These innovations included:
- Math orientation – array data, exponents, subprograms
- Optimized compiling – efficient machine code
- Structured
DOloops and conditionals READ / WRITEbuilt-ins for file I/O- Portability between computer systems
Over six decades later, FORTRAN still sees widespread usage across domains like science, engineering, and financial modeling requiring number crunching. Legacy systems written in FORTRAN code drive areas from weather prediction to particle physics analysis thanks to unparalleled performance.
Concepts pioneered in FORTRAN like variables, expressions, loops, remain integral across programming today. Even innovative areas like machine learning rely heavily on FORTRAN legacy libraries like BLAS and LAPACK for speed. FORTRAN also gave rise to BASIC in 1964 – designed for accessibly teaching coding.
Fun Fact: As of 2022, FORTRAN code still powers 9 of the world‘s top 10 supercomputers!
7. COMTRAN – Table-Driven Business Programming
While FORTRAN addressed science and math programming, business computing lacked languages tailored to tasks like financial reporting, data entry, payroll.
To pioneer business language capabilities in 1957, Bob Bemer led IBM‘s COMTRAN development – COmmercial TRANslator. COMTRAN introduced several innovations facilitating business applications:
- Picture clauses defined field formats – e.g.
PIC 99/99/99denoted date fields. This standardized data handling. - Table-driven logic enabled reuse – program logic referenced external customizable tables.
- Indexed file access improved performance analyzing large data sets as with payroll.
These built-in capabilities allowed businesses to adapt programs faster to changing needs – new reports, different formats, updated logic. No longer requiring vendor dependency!
While limited by lacking robust error recovery, COMTRAN set important precedent on optimizing languages for commercial data processing. It pioneered concepts adopted into COBOL shortly thereafter – leading language for business programming.
Fun Fact: COMTRAN was nicknamed "Bob‘s language" after creator Bob Bemer!
8. ALGOL – Vision of a Universal Language
During the late 1950s, enthusiasm across academia and industry rose for a universal language – suited for both numerical / scientific computing along with business purposes. FORTRAN and COMTRAN made strides in those niches, but a singular language with wide capabilities could maximize productivity.
Culminating these efforts was the design release of ALGOL 58 – ALGOrithmic Language. First specified in 1958 by committees involving John Backus, Friedrich Bauer, Peter Naur, Charles Katz, and others, ALGOL sought to unify the best elements of earlier languages:
- Math notation translated to efficient code (FORTRAN)
- Business-style statements and datatypes (FLOW-MATIC)
- Structure code blocks and scoping (IPL)
While ALGOL itself gained limited adoption, its pioneering vision opened the floodgates to future language development greatly accelerating software capabilities over the 1960s. Core concepts popularized by ALGOL include:
- Block structure – subroutines with local variables
- Nested code hierarchies
- Reserved keywords like
BEGIN,END,IF - Call-by-name parameter passing
- Symbolic representations of logic
These structured programming principles heavily influenced successors like Pascal (1970), C (1972), Java (1995), and more. So while 50 years lapsed since ALGOL‘s inception, its vision for rich, symbolic, portable code still permeates modern languages powering software that connects our world!
The Lasting Influences Live On
We covered 8 pivotal languages that pioneered programming concepts in the 1940s-50s era, setting computing on a path to where it is today:
Assembly Language – Abstraction from cryptic machine code using mnemonics
Short Code – Automating math translation for scientific programming
Autocode – Intermediate file compilation approach
FLOW-MATIC – English syntax and business data capabilities
IPL – Lists and recursion for complex data algorithms
FORTRAN – Math optimizations and high performance
COMTRAN – Table-driven logic and picture clauses for business computing
ALGOL – Structured programming and vision of code portability
These languages introduced capabilities while limited compared to modern languages, were groundbreaking inventions in their time. The programmer-visionaries behind them architected key concepts like high-level declarations, control structures, code portability, and rich data manipulation whose ripple effects still empower software developers today.
Understanding this heritage helps better leverage languages now and appreciate challenges of early computing that existed before programmers could import TensorFlow!
