High-Level Programming Languages: What Are They?

What are high-level programming languages? Everything you need to know about high-level languages and their benefits:

What Is A High-Level Programming Language? Definition

High-level programming languages (or HLPLs) are designed to be easily readable and understood by humans, providing a higher degree of abstraction from the underlying hardware. They simplify the programming process by using more natural language constructs and syntax, which allows developers to focus on problem-solving and logic rather than hardware-specific details.

The development of high-level programming languages dates back to the 1950s, marking a significant breakthrough in software development. Born out of necessity to simplify programming tasks and make them more accessible to a broader audience, high-level languages provided a higher degree of abstraction from the underlying hardware.

The introduction of languages like FORTRAN and COBOL made it possible for programmers to write code in a more human-readable form, resembling everyday language rather than the cryptic instructions of assembly or machine code.

The Impact of High-Level Programming Languages on Software Development

The impact of these languages on software development productivity has been nothing short of transformative. High-level languages enabled developers to focus on problem-solving and logic rather than wrestling with hardware-specific details and cryptic instructions.

The shift to high-level languages revolutionized the programming process by making it more efficient, allowing developers to write code faster and with fewer errors. This, in turn, reduced the time and effort required to bring new software solutions to market.

As a result, the software development community saw a surge in creativity and innovation, with a broader range of individuals and teams contributing to the field.

What Problems Did High-Level Programming Languages Solve?

High-level programming languages addressed multiple challenges, such as:

This shift enabled developers to write software for a wide range of applications, including scientific computing, business data processing, and system programming. Languages like FORTRAN were instrumental in solving complex mathematical problems in areas like physics and engineering, while COBOL became the backbone of many business and financial systems.

As high-level programming languages were invented and gained popularity, the use of low-level languages gradually decreased for many everyday software development tasks. However, low-level languages have not become obsolete. They continue to be used in specific scenarios that require precise control over hardware, like operating system development, embedded systems, and performance-critical applications.

The emergence of high-level programming languages was a milestone in the history of software development, driven by the need for more accessible and efficient ways to create software. Their development laid the foundation for countless innovations and advances in software development.

What’s the Difference Between High-Level and Low-Level Programming Languages?

Low-level programming languages, such as assembly language, are closer to the hardware level and require a more intricate understanding of the underlying computer architecture. While these languages provide a higher degree of control over hardware resources, they often come with a steeper learning curve and require more time to write and maintain code.

High-level languages work great in scenarios where ease of use, rapid development, and cross-platform compatibility are your priority. On the other hand, low-level languages are often better suited for tasks that demand precise control over hardware, such as operating system development, embedded systems programming, or performance-critical applications.

Code Example: Python vs Assembly

High-level Code: Python

In this Python example, the code is straightforward and close to natural language. The variables a and b are directly assigned numerical values, then added together, and the result is printed using a simple command. The high-level nature of Python abstracts away the details of memory management, binary arithmetic, and machine instructions.

Low-level Code: Assembly

In this assembly language example, the operation involves specific CPU registers (AX, BX). The programmer must manually move values into these registers before performing the addition. Additionally, printing the result is not a straightforward task as in Python; it typically requires calling a procedure that handles the low-level details of outputting to a display or console.

Key Differences Between Low-Level and High-Level Programming Languages

High-level Programming Languages: Execution Methods

High-level programming languages can be executed in various ways, primarily depending on their design and the environment in which they operate.

As a reminder: "execution" refers to the process by which a computer carries out the instructions in a program's code to perform the intended tasks. This involves translating the program's high-level instructions into a form that the machine can understand and act upon.

Execution can vary depending on whether the language is compiled, interpreted, or processed in another manner, but ultimately, it results in the computer performing the operations specified by the code, such as calculations, data manipulation, input/output operations, and other algorithmic actions.

Here are the main types of execution:

1. Compiled Execution

In compiled languages, source code is translated into machine code by a compiler before it is run. The machine code is specific to the target computer's architecture.

Example Languages: C, C++, and Rust.

Pros: Typically faster execution after compilation, as the machine code is directly run by the computer's CPU.

Cons: Requires a separate compilation step before execution; compiled code needs to be recompiled for different platforms.

2. Interpreted Execution

Interpreted languages are read and executed line by line by an interpreter at runtime, without the need for prior compilation.

Example Languages: Python, Ruby, and JavaScript.

Pros: Easier to debug and more flexible; code can be run on any machine with the appropriate interpreter.

Cons: Generally slower than compiled code since each instruction is read and executed on the fly.

3. Just-In-Time (JIT) Compilation

A hybrid approach where the source code is compiled into intermediate bytecode, which is then compiled into native machine code at runtime as needed.

Languages like Java and C#, which run on the Java Virtual Machine (JVM) and the .NET Common Language Runtime (CLR) respectively, have popularized JIT compilation. This method offers a balance between the speed of compiled languages and the flexibility of interpreted languages. The increasing focus on web and mobile applications, where performance and quick startup times are crucial, has made JIT compilation particularly attractive.

Example Languages: Java (via the Java Virtual Machine) and C# (via the Common Language Runtime).

Pros: Balances the flexibility of interpretation with the speed of compilation; improves performance over time.

Cons: Initial execution may be slower as code is compiled; requires a runtime environment.

4. Ahead-of-Time (AOT) Compilation

Compiles bytecode into machine code before execution, similar to traditional compilation, but starting from an intermediate form.

This method has been gaining popularity with the rise of frameworks and platforms like .NET Core and the increasing use of languages like Swift and Kotlin. AOT offers faster startup times compared to JIT, which is beneficial for mobile apps and cloud-based applications where quick scalability is important.

Example Languages: Used in some environments for languages like Java and C#.

Pros: Provides faster startup time compared to JIT compilation and can improve runtime performance.

Cons: Loses some of the platform independence of bytecode; requires recompilation for different targets.

5. Transpiled Execution

Source code is converted (= transpiled) into another high-level language, which is then compiled or interpreted according to the target language's standard practices.

Example Languages: TypeScript (transpiled to JavaScript), Babel (transpiles modern JavaScript to older versions).

Pros: Allows developers to use newer language features or different languages while targeting platforms that support another language.

Cons: Adds complexity to the build process; dependent on the capabilities of the target language.

While traditional compiled and interpreted methods remain common, JIT and AOT compilation methods are increasingly favored due to their performance benefits and suitability for modern application development trends.

List of High-Level Programming Languages

Python, JavaScript, Java, and other popular languages have active communities, extensive libraries, and industry support, which makes them well-positioned to tackle emerging challenges and opportunities. These languages have proven their flexibility and adaptability in diverse domains, from web development and data science to mobile app creation and artificial intelligence.

As long as developers and the tech industry continue to invest in these languages and their ecosystems, they'll remain relevant and popular choices for both seasoned professionals and newcomers alike.

Pioneering High-Level Programming Languages in the 20th Century

Languages like COBOL, FORTRAN, Lisp, ALGOL, and Pascal were pioneers in their time, helping shape the landscape of modern programming. They've gradually fallen out of use for a variety of reasons.

For instance, COBOL and FORTRAN were designed for specific purposes, such as business applications and scientific computations, respectively. As the industry's focus shifted towards more versatile and general-purpose languages, these older languages became less relevant.

Lisp and ALGOL, though innovative in their time, were eventually overshadowed by languages that were more user-friendly and had more extensive libraries and community support. Pascal, once a popular language for teaching programming concepts, has been largely replaced by more modern languages that better reflect today's programming practices.

In essence, the rise of more powerful, versatile, and user-friendly high-level programming languages has caused these early trailblazers to become less prevalent today.

What Are Very High Level Programming Languages?

Very high-level programming languages, often referred to as fourth-generation programming languages (4GL) or higher, are designed to be more abstract and closer to human language compared to lower-level programming languages.

They aim to provide a higher level of abstraction, making it easier for non-programmers or domain experts to interact with systems, automate processes, or handle database management and manipulation with less concern for traditional coding structures like loops and conditionals.

Some examples: SQL, MATLAB, SAS, R, ABAP, LISP, Prolog, Haskell, Erlang.

The Purpose of Very High Level Programming Languages

The primary purpose of very high-level programming languages is to increase developer productivity, reduce coding errors, and make the software development process faster and more intuitive.

They achieve this by:

Very High Level Programming Languages: Examples & Usage In 2024

Examples of very high-level languages include:

Other notable examples are languages focused on specific business sectors or applications, such as ABAP (used in SAP software for business applications) and R (used primarily for statistical computing and graphics).

The extent to which these languages are used can vary greatly depending on the industry, specific job roles, and current technology trends. For example, SQL is nearly ubiquitous in roles that involve database management or data analysis, while MATLAB might be more specifically utilized in engineering disciplines.

Despite the rise of more general-purpose programming languages like Python and JavaScript, very high-level languages retain significant usage due to their specialized capabilities and the efficiency gains they offer for specific tasks and industries.

I hope this guide helped you gain a deeper understanding of high-level programming languages.

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