The Most Popular Programming Languages in 2023

As the practice shows, no idea, even the most brilliant one, when embodied in a digital product or service, will become successful without quality assurance. It, in turn, always includes two non-intersecting procedures that occur before software is launched: the first of them is aimed at obtaining a clear understanding of whether development is moving in the right direction, and the second of them clarifies whether the solution being created meets end user needs. So, how are they called and performed? Let’s find it out right now.

What Is Verification Testing?

In a nutshell, it is a procedure aimed at checking whether the solution being created meets its predefined specifications and generally accepted design and development standards. Software verification testing is performed throughout the entire creation process and involves the use of various automation testing tools and verification methods, such as modular, integrational, and system testing.

What Is Validation Testing?

Unlike the previous type of testing, this one involves planning real-world scenarios that allow the development team to make sure that the solution being created meets its audience’s needs (for this, you can apply, for example, the alpha- and beta-testing validation methods). After that, the solution has to be compared with its specifications. If any discrepancies are found, they need to be eliminated before this solution is released to the public. Actually, that’s why validation testing is repeated until the team achieves full compliance.

Purpose of Verification and Validation

The main goals of verification and validation testing are to fully satisfy the needs of the software solution owner and the audience as well. As soon as these procedures are successfully completed, the project can be launched.

Difference Between Verification vs Validation Testing

So, what about the verification and validation difference? Generally speaking, while verification is aimed at checking the compliance of a product or service with its list of requirements agreed upon between the development team and the owner, validation allows teams to make sure that this service or product will be positively accepted by its end users. 

In this regard, the first procedure starts from the very beginning of the development process and lasts until the final testing stage and the second—only after the product/service is completed. This also imposes certain differences on the test data used: during verification in software testing, QA engineers use sample data (in this regard, automation methods are often applied), and during the software validation—real data (usually, manual methods do a great job here).

How Are Verification and Validation Testing Performed?

Now, let's look at the four most popular approaches used for validation and verification in software testing:

• Peer reviews, which involve providing a software solution to focus group representatives for a certain period of time in order to obtain feedback on their experience of interacting with it;
• Assessments, which are left by focus group representatives after checking the solution for functionality and identifying errors and inconsistencies in it;
• Walkthrough, which implies a preliminary explanation to focus group representatives of how exactly to interact with the software solution, after which the latter leave their feedback on how well everything was clear and whether they think some areas need to be optimized;
• Desktop-checking, which includes reviewing the software code by specialists (in particular, software engineers).

Tips for Successful Verification and Validation

To ensure these two procedures go smoothly, follow these guidelines:

1. Define the tasks and goals you are pursuing through these procedures and what the results should be upon their completion;
2. Choose the specific tools and approaches you are going to use—they should cover all possible scenarios of user interaction with your software solution, as well as generally accepted software development standards;
3. Create a testing plan, where you will specify deadlines, available resources, and evaluation criteria (along with the acceptable minimum compliance);
4. Proceed directly to testing in order to identify bugs and inconsistencies that will need to be fixed before the project is released;
5. Involve representatives of the potential target audience and stakeholders in the testing procedure to get the most objective assessment of the quality of your team's work;
6. Don't forget to document and monitor the testing results from one bug fixing to another to understand the dynamics of improvements.

Popular Test Automation Tools For Verification and Validation

Although automated testing is considered a standard approach for verification testing only, sometimes, it also brings benefits when implementing validation testing. In this regard, we have compiled a small selection of popular tools that you can use for quality control in software:

• Selenium, an open-source web solution that supports a lot of programming languages, such as Java, Python, C#, and so on, as well as integrates with different frameworks;
• Appium, an open-source service for automated testing of mobile apps on iOS, Android, and Windows (both native, cross-platform, and hybrid ones);
• Robot Framework, an automation framework aiming at implementing acceptance testing and acceptance test-driven development;
• Jenkins, an automation server used for continuous integration and continuous delivery that streamlines the build, test, and deployment stages;
• TestNG, a popular testing framework that is actively used for testing Java-based software solutions;
• Cucumber, an environment for running automated acceptance tests written in Java, Ruby, etc.;
• JMeter, a perfect solution for performance testing of web apps.

Conclusion

We hope that we have helped you understand the difference between validation and verification testing, and now, you can also assess their importance in creating high-quality and competitive software solutions. You can also check out our blog to learn more about software development and design.

In the design and operation of digital products, it is essential to detect errors and ensure that these products fully comply with specifications (both generally accepted and defined by their owners). For example, in the field of healthcare, using such solutions directly impacts the health and life of patients and the level of efficiency of medical services. So, let’s delve into this topic. 

Objective of Quality Assurance in Healthcare App Development

So, let’s find out why QA is important. In general, it is one of the integral parts of software development, especially in the medical field. After all, in the field of medical services, the fate of the patient's life and health is decided every day. Digital solution that looks and operates as intended (of course, thanks to the quality assurance benefits) provides the ability to prevent all potential errors as efficiently as possible and ensures that applications meet modern medical standards. 

Regarding the importance of quality assurance in healthcare applications, the following points can be distinguished:

• Safety of patients: At the heart of QA in healthcare software development is patient safety. The timely testing of healthcare applications that could impact a patient's health is a critical aspect. This includes verifying medicine dosage, ensuring the accuracy of diagnostic algorithms, and the functionality of the patient monitoring system. Any malfunction, even for a brief period, can lead to serious errors, underscoring the importance of reliability assurance.
• Data privacy: The guarantee of absolute protection of patients' personal data from unauthorized access. Due to their specificity, medical applications regularly process a huge amount of information, so the introduction of modern encryption methods is critical.
• Compliance with regulatory requirements: A number of modern healthtech standards like HIPAA and GDPR are created for a reason. Therefore, absolutely all software should be developed to meet these requirements. Civilized countries actively implement official app development rules. If these laws are ignored, it is undoubtedly a direct path to legal accountability, with all the attendant consequences. The implementation of reliable digital solutions reduces the chances of getting a fine or a lawsuit until the loss of a license.
• Optimization of regular processes: The higher the efficiency of the application, the better the performance and accuracy of work within the medical professional team. In this way, medical staff can access the necessary information, thus minimizing the time spent on data processing. This guarantees both speed and high accuracy of decision-making regarding the further treatment of patients.

Risks Associated with Poor Software Quality in Healthcare

No one will argue that some medical institutions ignore the risks of using legacy software that doesn’t meet the modern healthtech regulations. However, this is a very serious and critical topic that can significantly impact patients' safety and lives and the effectiveness of daily processes.

Numerous errors, incorrect diagnoses, and, as a result, incorrectly prescribed treatment all directly threaten the patient's life and health. Also, do not exclude the occurrence of legal and reputational problems (fines or a prominent indicator of loss of confidence on the part of the public, both patients and employees of a particular medical institution). Unreliable digital solutions automatically complicates the interaction between employees. It leads to a decrease in the productivity of daily medical practice because it complicates the possibility of uninterrupted access to all necessary data and the exchange of information between departments and specialists.

Types of Testing in Healthcare Applications Development

There are different types of healthcare application testing, but the most common and popular among them are the following types:

• Performance testing: It includes analyzing the application's speed of action and navigation, taking into account different levels of load.‍
• Regression testing: It defines how much each of the module functions meets the requirements.‍
• Security testing: It implies checking the protection of each patient's data from illegal access by unauthorized users.‍
• Usability testing: This example of quality assurance in healthcare is designed to evaluate how user-friendly the application is, ensuring it is convenient for both healthcare industry employees and patients of all ages.

Healthcare app testing features are primarily critical, including the need for strict compliance with regulatory requirements and safety regulations.

The Future of Quality Assurance in Healthcare

Now, knowing the answer to the question: “What is quality assurance in healthcare?”, let’s talk about its future. Specifically, today, we can define three the most sustainable trends:

• Implementation of AI and ML-based solutions aimed at automating the testing process, independently identifying bugs, and preventing them in advance;
• Combination of DevOps and Agile, which ensures closer interaction between development and testing teams, ultimately reducing the number of bugs and speeding up their fixing;
• Using cloud-based testing platforms that provide scalability of QA healthcare scenarios without compromising the budget.

However, in the coming years we can expect something more revolutionary than the actualization of the above-mentioned directions since the healthtech sector is actively invested in by both governments and private institutions.

Conclusion

Now, you know the answer to the question: “What is quality assurance?” and can understand the role of QA in software testing—generally speaking, it is crucial regarding ensuring patient safety and the performance of the healthcare delivery process. Minimizing risks during the medical quality assurance process and creating reliable medical applications are possible only through the use of modern technologies and compliance with all official standards provided by a qualified service provider.

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Top 10 Programming Languages for Artificial Intelligence in 2025 

It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?

Python 

At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.

One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.

Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.

Best suited for: AI development, data analysis, and machine learning.

Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.

The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.

R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.

Best suited for: Statistical analysis, data processing, and visualization in AI projects.

Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.

Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.

Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.

Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.

Weakness: Verbose syntax, not specifically designed for AI.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.

Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.

Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.

Best suited for: Developing high-performance AI applications with hardware resource control and memory management.

Weakness: A complex language designed for system programming.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).

Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.

Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.

With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.

Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.

Weakness: Not well-suited for mathematical computations and machine learning.

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this—thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

It allows writing clear and efficient code, facilitating the creation of high-performance applications.

One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.

Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.

Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.

Best suited for: Developing scalable AI applications with big data support and real-time information processing.

However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.

Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.

With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.

As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.

Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.

Weakness: Newer language, smaller ecosystem.

Haskell

Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.

The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.

Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.

Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.

Weakness: Abstract, rarely used in the field of artificial intelligence.

Lisp

Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.

The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.

Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.

Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.

Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.

Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.

Prolog

Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.

Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.

The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.

Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.

Weakness: Limited usage and a small number of libraries.

What Is the Most Popular Programming Language Used in AI in 2025?

Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.

In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.

The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.

Choosing the Best Programming Language for AI Development

From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.

Here are a few steps that will help you choose the best programming language for your next AI project:

For Your Project

Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.

Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.

For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.

To Learn

Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.

By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.