A programming language is the foundation for developing any kind of software. Different languages are designed for specific tasks—front-end and back-end development, machine learning, scientific data collection, and mobile applications.
According to
Stack Overflow, Python remains one of the leading languages thanks to its strong ecosystem in AI/ML and data science (referring to its extensive libraries and frameworks).
Source: TIOBE
More than 60% of professional developers prefer core languages such as JavaScript, HTML/CSS, SQL, and Java.
Source: DeveloperNation
At the same time, new languages are gaining popularity—namely Rust and Go, due to their focus on safety, as well as TypeScript, which is very similar to JavaScript but offers stronger safety and type-checking.
Introduction to Programming Languages
Without programming languages, it would be impossible to imagine a web application or the operation of a system tool. Each language serves its own purpose—from performance optimization to ease of use. There are also languages specifically used for data analysis or for interacting with hardware.
Over the past several decades, programming languages have become an indispensable element in the development of many fields. In the United States and Europe, the most popular languages are Python, JavaScript, and Java.
Source: Stack Overflow
For instance, the
Python programming language is widely used in the field of artificial intelligence, as well as for writing various scripts. JavaScript is the foundation for front-end development, as well as back-end and fully functional tool systems. Without
Java, it is impossible to imagine the operation of Android and enterprise systems.
Each language has its own unique features and supports a specific set of paradigms. Several key concepts include:
- OOP – Object-Oriented Programming. The idea is to organize code into objects that combine data and behavior.
- FP – Functional Programming. This paradigm emphasizes pure functions, immutability, and a declarative style.
- Scripting Languages. Flexible, dynamically typed languages characterized by automation and the creation of glue code (e.g., Python, JavaScript, shell scripts).
The trends of 2025 set the criteria for choosing the tools that will be most useful for developers in their work:
- Languages with strong ecosystems in artificial intelligence and machine learning are in the spotlight, with Python playing a key role.
- For boosting performance, efficiency, and addressing system-level challenges in cloud technologies, the Internet of Things, and big data processing, languages like Go and Rust prove highly effective.
- For web development, mobile applications, and cross-platform solutions, JavaScript, TypeScript, Swift, and Kotlin are ideal choices.
Source: ITransition
- Mixed and multi-paradigm languages allow developers to remain flexible in their work and enhance the competitiveness of new projects. In practice, this means combining OOP with functional and procedural programming, which helps create highly secure code while reducing dependency on external changes.
By focusing on their specific needs, each developer can independently choose the most effective toolkit, taking into account performance, scalability, security, cross-platform compatibility, and more.
Categories of Programming Languages
All programming languages can be grouped into several target categories, each corresponding to a particular style and paradigm. These categories are presented in the table below.
Category |
Key Features |
Strengths |
Weaknesses |
Use Cases |
Procedural (Imperative) |
Languages in which programs are built as sequences of instructions (procedures or functions) that modify the program’s state;
focus on statements, control flow (for, while), and modularity through functions. |
A simple language model, suitable for learning and tasks that require step-by-step control; generally efficient in execution; easy to map to low-level, hardware, or system-level tasks. |
Can lead to code that is harder to maintain in large systems (due to side effects and shared mutable state); offers fewer opportunities for parallelism; provides less expressive abstraction for certain tasks. |
C – a classic example of a procedural language; used in many system and embedded codes; parts of C++ and Go also follow procedural paradigms. |
OOP (Object-Oriented Programming) |
Languages that organize code into “objects” that encapsulate data and behavior; support inheritance, polymorphism, and encapsulation. |
Well-suited for modeling real-world entities; help structure large codebases; promote code reuse; many frameworks, tools, and IDEs offer strong support for OOP. |
Can introduce overhead both at runtime (e.g., virtual dispatch) and during design (complex hierarchies, tight coupling); may lead to over-engineering; risk of reduced performance. |
Java, C#, C++, Python, Ruby; many large enterprise and mobile applications are built using OOP. |
FP (Functional Programming) |
Focus on pure functions (without side effects); immutable data; first-class and higher-order functions; expressions instead of statements; recursion instead of loops. |
These languages facilitate reasoning and testing; offer better support for parallelism due to the absence of shared mutable state; often enable concise code; well-suited for data transformation and machine learning pipelines. |
May have a steep learning curve; recursion can be inefficient without compiler optimizations; may be less intuitive for developers accustomed to imperative styles; performance can sometimes be a concern. |
Haskell, Scala (multi-paradigm), F#, as well as the functional features of Python, JavaScript, and Rust. |
Declarative (Logical) |
The essence of this language model is that instead of specifying how to do something, the developer defines properties, constraints, or relationships; the language or system determines on its own how to satisfy these requirements |
These languages are useful for knowledge systems, rule engines, automated reasoning, and constraint solving; they can reduce code for certain types of tasks and offer high expressiveness for logic- or AI-based applications. |
Not general-purpose languages; performance can be unpredictable; often have fewer libraries and less mature ecosystems; debugging can sometimes be more difficult. |
Prolog, Datalog, and languages or frameworks for constraint logic and rule engines. |
Scripting (Interpreted) Languages |
Languages that are often interpreted (or JIT-compiled), with dynamic typing; designed for rapid development, creating glue code, and automation; may incorporate features from OOP, functional programming, and more. |
Fast iteration; ease of use;
low entry barrier; ideal for scripting, prototyping, front-end, and back-end tasks; strong communities and well-developed library ecosystems. |
Performance is often lower; dynamic typing can lead to errors; scaling or maintaining large codebases can be more challenging without strict discipline. |
JavaScript, Python, Ruby, PHP, and others. |
According to a
TIOBE study, Python is the leading programming language this year with a rating of 25.98%. Following it are C++ at 8.80%, C at 8.65%, Java at 8.35%, and C# at 6.38%.
The statistics presented in the table indicate that languages with strong multi-paradigm support (combining OOP, FP, and procedural approaches) hold leading positions in development, and this trend is expected to continue next year. At the same time, specialized languages such as C and C++ remain critically important for system-level, embedded, and performance-critical applications. Such developments would be impossible without them.
According to
Crossover, interest in functional and multi-paradigm capabilities is expected to grow next year. Languages that allow mixing paradigms are increasingly used, providing high levels of safety, multithreading, and parallelism.
Additionally, in web development and AI-related tasks
, the most in-demand languages are Python, JavaScript, Java, C#, Swift, TypeScript, Kotlin, Ruby, Rust, and Go.
The most in-demand programming languages
As the technological landscape rapidly evolves with the adoption of artificial intelligence, cloud technologies, and cross-platform approaches, many programming languages remain highly in demand for tackling current challenges.
The table below presents the most in-demand programming languages.
Programming language |
Why it is important in the field of development |
Advantages and capabilities |
Key challenges |
Python |
It is the best choice for data science, machine learning, artificial intelligence, scripting, and back-end development; it has an extensive ecosystem (libraries such as TensorFlow, PyTorch, scikit-learn); and it is easy to learn. |
High productivity for prototyping, research, and data analysis; extensive support and tools; an excellent bridging language. |
Lower performance compared to compiled languages; not suitable for projects where scalability, speed, or strict memory/latency requirements are critical. |
JavaScript/TypeScript |
JavaScript is an indispensable tool for front-end development, while TypeScript is gaining popularity due to its static typing and better support for large codebases. |
Extensive ecosystem; universal browser support; TypeScript enhances code safety. |
JavaScript sometimes suffers from performance limitations in large systems; its dynamic behavior can lead to errors; TypeScript requires specific tooling and build configuration. |
Java |
Widely used in enterprise environments, Android applications, and back-end services; offers stability, a long history, and large codebases. |
A reliable and mature ecosystem; powerful tooling; backward compatibility; a large pool of skilled professionals. |
Some parts of the ecosystem are heavy and complex; memory usage and startup time, especially for microservices, can be less optimal compared to lighter languages. |
C++ |
It is the primary language for developing performance-critical applications, such as games, embedded systems, real-time systems, and infrastructure. |
High performance; precise control over memory and hardware; mature compilation; large and well-established codebase. |
Writing secure code is more challenging; memory management errors; steeper learning curve; iterations are slower than in high-level languages. |
Go (Golang) |
The language is increasingly used due to the growing demand for cloud infrastructure, microservices, and DevOps, where scalability, simplicity, and back-end performance are crucial. |
Simpler concurrency model; fast compilation; good performance; strong support for cloud, container, and microservices environments. |
Some specialized libraries are less mature compared to older languages; fewer developers with extensive experience; the language is not suitable when ultra-low latency or highly constrained resources are required |
Rust |
It is a popular language in the low-level systems, performance, and security segments, as well as in security-sensitive code. |
Memory safety; thread safety; performance close to C/C++; modern syntax; robust tooling; growing adoption in infrastructure, embedded systems, WebAssembly, and systems programming. |
Smaller ecosystem compared to more widely used languages; tooling and libraries in some areas are less developed; steeper learning curve for some developers; compilation time can be longer. |
R, Julia |
These languages are highly specialized but important in areas such as data science, research, and numerical and statistical computing. |
High performance for numerical tasks; Julia aims to combine speed with ease of use; R continues to be widely used in statistics. |
Less commonly used in production web applications; fewer specialists available; integration with other parts of the tech stack can sometimes be less seamless. |
According to
Huntr.co, Rust developers in the U.S. market had the highest salaries in Q2 2025 – nearly $261,000 per year. Golang developers earn around $196,000 per year, while
TypeScript specialists make approximately $183,500 annually.
An IT market analysis by Signify Technology shows that over the past year, the number of Go development job openings increased by 17%. A study by
Itransition notes that 45% of recruiters worldwide are looking for Python specialists, with JavaScript developers in second place at 41.5%, and Java in third at 39.5%.
The most popular programming languages
Based on the
TIOBE Programmer Community Index, a ranking of the most popular programming languages has been compiled.
At the time of writing, the ranking is as follows:
- Python;
- C++;
- C;
- Java;
- C#;
- JavaScript;
- Go;
- Visual Basic;
- Perl;
- Delphi/Object Pascal;
- JavaScript;
- PHP;
Source: TIOBE
This popularity ranking is based on the evaluation of the following factors:
- Ease of use and speed of learning;
- Flexibility and multi-purpose applicability;
- Availability of mature frameworks and libraries (web frameworks, machine learning/data processing tools, graphical interfaces, etc.);
- Community size and growth dynamics;
- Performance and suitability.
Other programming languages
In addition to the popular programming languages described above, there are many others gaining traction due to modern features, specific domains, or the needs of legacy/system-level solutions.
They are presented in the table below, along with their key characteristics.
Programming Language |
Popularity Factors |
Key Advantages |
Usage Challenges |
Language Usage Statistics |
Kotlin |
Increasingly popular in Android development; used in server-side and cross-platform projects; many teams prefer Kotlin over Java for new Android applications due to its concise syntax, null safety, and modern tooling. |
Compatibility with Java (ability to use existing Java libraries); cleaner syntax; coroutines for concurrency; cross-platform capabilities (Android, JVM, web applications via Kotlin/JS, Kotlin Multiplatform). |
For existing large Java codebases, migration costs can be high; smaller ecosystem compared to Java in certain areas; fewer developers with extensive experience outside Android contexts; perceived performance decrease in some JVM configurations. |
According to Itransition, over 60% of professional Android developers are using Kotlin in 2025. |
Swift |
The primary language for iOS/macOS development; its modern syntax, safety features, and Apple support make it the standard for new applications on Apple platforms; there is growing interest in back-end development through Swift-on-servers. |
Performance; safety; modern features; strong integration with the Apple ecosystem; increasingly mature tooling. |
Primary use is on Apple platforms (iOS, macOS, watchOS, tvOS); less general-purpose; fewer back-end/server resources compared to languages like Java, Kotlin, or Go; smaller job market outside mobile and Apple-specific development. |
According to Itransition, approximately 6% of developers used Swift in the past 12 months. |
Scala |
Used in big data, back-end services, and distributed systems, especially with Apache Spark; suitable for projects requiring both functional and object-oriented paradigms. |
Expressiveness; powerful abstractions; well-suited for data transformation, stream processing, and batch processing; strong presence in ecosystems such as Spark, Kafka, etc. |
Steeper learning curve; sometimes more complex build or tool configuration; smaller talent pool; slower adoption outside its niches; JVM overhead and setup requirements. |
According to Devopsschool, the salary range for Scala developers in the U.S. and Europe is $75,000 to $175,000 per year. |
Operating Systems and Programming Languages
Operating systems include Windows,
macOS, Linux, and
ChromeOS. Mobile operating systems include
Android, iOS, and iPadOS.
Source: Statcounter
According to
Statcounter, the current distribution and usage of various operating systems across market segments is as follows:
- PCs and laptops – 71% Windows;
- Apple devices – macOS with a 16% market share;
- Linux (excluding ChromeOS) – approximately 5%;
- Mobile devices (smartphones and tablets) – Android accounts for 73%, while iOS and iPadOS make up 28%;
- Linux and Unix-like OSes are used in server and cloud environments.
Source: Statcounter
The interaction between an operating system and a programming language has a direct impact on the efficient development and deployment of software.
The relationship between OS and programming languages can be summarized as follows:
- Native platform support – some languages or tools are better integrated with specific OSes. For example, Swift is primarily suited for iOS/macOS, while Kotlin or Java are preferred for Android. On Windows, C#, .NET, and sometimes C++ are commonly used for system applications and drivers.
- Cross-platform capability – Java and Python allow code to run on different OSes with minimal changes (provided the appropriate environment/interpreter/JVM is available). C++ is also widely used in cross-platform applications (games, graphics, engines), often with conditional compilation or abstractions over OS-dependent code.
- Performance and low-level access – when tight control over resources is required (memory management, system calls, drivers, embedded and real-time systems), languages like C, C++, and Rust are relevant.
- Build and deployment infrastructure – OS differences affect build tools, CI/CD pipelines, and dependent libraries. POSIX-compliant systems (Linux, macOS) offer advantages for scripts, shells, Makefiles, Bash, Docker, etc.; Windows may have its own specifics (PowerShell, .NET, Microsoft tools).
According to the Stack Overflow Developer Survey 2025, nearly 57.9% of developers use Python, 29.4% use Java, while Go and Rust are used by 16.4% and 14.8% of developers, respectively.
The table below shows OS and programming language compatibility.
Operating System |
Programming Languages |
Application Areas |
Windows |
C#, C++, .NET, sometimes Rust, Python |
Desktop applications, business software, games (Unity/Unreal), utilities, GUI tools, enterprise software |
macOS/iOS |
Swift, Objective-C, Python, JS/TS (via Electron or web), sometimes C++ |
Mobile and tablet applications (iOS), multimedia software, graphics applications, cross-platform tools |
Linux/Unix-like / servers |
C, C++, Python, Go, Rust, Java |
Server applications, cloud services, network services, containerization, DevOps, automation |
Android |
Kotlin, Java, sometimes C++ (for performance-critical parts), Rust |
Mobile applications, games, parts of system software, cross-platform solutions |
Software Development
Software development is a full lifecycle of creating applications, including the following stages::
- Requirements gathering
- Design
- Implementation
- Testing
- Deployment
- Maintenance
Programming languages play a key role at every stage. They determine development speed, ease of maintenance, performance level, team collaboration, choice of tools, and the overall cost of the project.
The choice of programming language directly affects the development process:
- Rapid application development/prototyping: languages such as Python, JavaScript, and Ruby facilitate quick prototyping and iterations. They allow developers to test ideas faster, create minimum viable products (MVPs), and adjust features based on feedback without writing bulky boilerplate code.
- Scalability and maintainability: for large-scale systems or long-term projects, languages like Java, C#, TypeScript, Rust, and Go are suitable due to strict static typing, robust tooling, mature frameworks, and clear module/package systems.
- Performance and system constraints: where latency, memory usage, parallelism, or hardware interaction are critical (e.g., games, embedded systems, real-time systems, high-frequency trading), low-level or performance-oriented languages such as C, C++, and Rust are preferred.
- Team expertise, ecosystem, and library availability: for many developers, the compromise is choosing between a language the team already knows well and switching to a newer (better) language, which may require training and pose potential project risks.
A study conducted by
accentonpeople shows that during 2024–2025, over 6.1 million software developers in Europe specialize in web development, backend, and mobile applications. 62% of developers use JavaScript, 51% use Python, and 30% and 25% use Java and C/C++, respectively.
According to the
SlashData report, from 2022 to 2025, the number of C++ developers increased from 9.4 to 16.3 million. The Rust language is rapidly gaining popularity and has become the fastest-growing language this year.
The Best Programming Language
Today, there is no single perfect programming language; however, companies in the USA and Europe take a pragmatic approach to development. Each aims to optimize their solutions based on specific goals, often combining different languages.
Of course, when developing a Fintech startup, it is best to use Java or Kotlin to secure transactions. For a game studio, using C++ or Rust is important to ensure high project performance.
The key factors in choosing the best programming language include the following:
- Application type and domain: for web development, JavaScript and TypeScript (frontend) and Node.js, Python, Java, and Go (backend) are used. For mobile devices, the best languages are Swift (iOS), Kotlin (Android), and Flutter/Dart (cross-platform). In artificial intelligence, Python, Julia, R, and Rust are essential, while C, C++, and Rust are used for systems and embedded applications.
- Performance and scalability: languages like C++, Rust, and Go are important for systems requiring extremely high performance or parallelism, whereas Java, C#, and Kotlin are indispensable for enterprise applications.
- Security and reliability: Rust is becoming increasingly popular due to its memory safety guarantees.
- Team experience and hiring market: the most widely used languages in the global market remain JavaScript and Python.
Python Developers
Product development in Python is undoubtedly one of the most in-demand fields. The language is nearly universal, features a vast ecosystem of libraries, and is increasingly being used in artificial intelligence.
According to Stack Overflow 2025, from 2024 to 2025, Python’s popularity grew by 7%, placing it among the top programming languages in the world that developers will continue to use. In the USA, Python specialists can earn between $68,000 and $156,000 per year.
The main factors behind Python’s popularity and demand include the following:
- Universal language – used in almost all key areas: from backend to machine learning, from data analysis to automation, from scripts to DevOps utilities. It does not require extensive low-level coding, allowing faster prototyping and deployment of solutions.
- Extensive library and framework ecosystem.
- Learning curve – Python’s syntax is considered one of the most beginner-friendly and understandable. For many developers, Python becomes their first programming language, making it an important “entry ticket” to the IT market.
- AI/ML/Data Science driver – Python is suitable for experiments and research, creating prototypes in artificial intelligence and machine learning, where integrating ML tasks into finished products is required.
- Community and infrastructure – Python has a large and active community, numerous open-source projects, and a wealth of courses, documentation, and resources.
In 2026, Python’s popularity is expected to remain high, especially in the context of artificial intelligence projects. The market is already seeing the integration of the language with development automation tools, ML Ops, and data pipelines, where Python code becomes part of a larger distributed infrastructure.
Classic Visual Basic
Classic Visual Basic or VB6, was popular in the 1990s and 2000s as a rapid application development (RAD) tool on the Windows platform. Although official support for VB6 ended in 2008, the language continues to be used in certain industries, particularly for maintaining and updating existing systems.
Microsoft continues to ensure
VB6 compatibility with new Windows versions through the "It Just Works" program. However, the company does not provide new features or updates for VB6, limiting support to existing applications.
VB6 remains relevant in healthcare, retail, finance, and construction, where legacy systems require maintenance and updates.
Assembly Language
Assembly Language is a low-level programming language. It provides programmers with direct access to a computer’s hardware resources. Despite the development of high-level languages, assembly remains relevant in specific areas where maximum performance and precise hardware control are required.
Assembly Language is used in the following areas:
- System programming – development of operating systems, bootloaders, and other components requiring direct hardware interaction. This allows performance optimization and ensures reliable system operation.
- Embedded systems – microcontrollers and specialized processors are often programmed using assembly to achieve high efficiency and minimize resource consumption. Popular examples include consumer electronics, automotive systems, and medical devices.
- High-performance computing – for tasks requiring maximum data processing speed, such as scientific computing and simulations, assembly allows code optimization for a specific processor architecture, achieving peak performance.