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There are about 8.93 million mobile apps worldwide, with new ones popping up daily, but not each of them has a good mobile interface design. At the same time, those who have it, win the hearts of customers and earn millions of dollars. So, how can you achieve this goal? Let’s find it out right now.

What Is Mobile App Design?

Mobile app design is the visual identity of an app tailored to the user's behavior. It has two components: UI and UX. UI (user interface) is how the application looks, and what colors, fonts, icons, buttons, or other elements it contains. UX is how an app works, responds to user actions, and helps fulfill people’s needs.

Interface design for a mobile application calls for consideration of the specifics of mobile platforms, such as screen size, data input methods, battery level, and internet connection speed. You should bear in mind that design can have a big impact on the success and popularity of the product, its usability, and its attraction to users.

Key Principles of Mobile App Design

Creating the interface of mobile applications is a truly complex and multi-layered process. It can vary dramatically depending on what project the app is created for, who the target audience is, and what functionality its owner is looking for. However, there are a few fundamentals to keep in mind when designing.

Here are top basic mobile design principles:

1. Prioritize the user’s needs 
2. Focus on visual design
3. Make the app interface convenient
4. Make the content easy to read
5. Add some interaction
6. Optimize the interface for different mobile devices

User Interface (UI) Design Principles

Let’s define the most important user interface design principles.

Simplicity

An eye-catching and straightforward user interface is ideal. There shouldn't be too many interface elements that can confuse a user. Nobody wants to explore a new interface once an application is downloaded—instead, people just want to get what they need or have fun.  All they seek is simple products that “just work”, that’s why, you have to prioritize the main parts for design and remove the unnecessary ones.

There is a simple rule of thumb: one main action per screen. Each screen you design in your application should support a single, genuinely important action for the user. This makes it easier to learn and use. One hundred clear screens are better than a single cluttered one.

Consistency 

The interface has to be intuitive and easy to navigate. You can add a great feature or content, but if people can't find it, it doesn't matter. Mobile navigation should be consistent. To ensure navigation doesn’t require any explanation, you should use the right visual metaphors, and check that each navigation item leads to the appropriate place.

Feedback and Response Time

Feedback informs users whether they are doing the proper action or not. It can be audible (the ding of a new message notification), tactile (the vibrating signal for a new email or call while your phone is set to "silent"), or just visual. Feedback should be provided after each operation to show if it was successful. 

Feedback can be useful in responding to problems in four areas:

1. Locating: Where are you?
2. Current Situation: What's happening? Is it still going on?
3. Prospects: What comes next?
4. Effects: What did you get?

Quick, insightful, and observable feedback is essential to let people know their actions were noticed. The link between the activity and its effect should be clear so that users understand what happened, and what to expect next. Feedback is supposed to optimize and complement the user's experience rather than complicate it.

User Experience (UX) Design Principles

Now, we propose to list the key UX design principles.

Usability

Usability measures how easy and convenient it is for the user to interact with the interface of an application. It depends on the ease of navigation, structure, content quality, text layout, mobile app design, and a bunch of technical features. This aspect affects users' time on an app, conversion rates, and customer loyalty. 

Here are five components of usability:

1. Learnability: how easy is it for people to perform basic tasks the first time?
2. Productivity: how quickly can people complete tasks?
3. Memorability: when people come back to an application after a time out, how easily can they recover their skills?
4. Error recovery: how many errors do users make, how serious are they, and how easily can people fix them?
5. Enjoyment: How engaging is it to use the app? Does it help people accomplish their tasks? 

User-Centric Design

User-centered design is an approach that consistently identifies user needs and translates them into a product. The core value is to focus on what people are used to, on what is “easy” for them and meets their basic needs.

Figure out what users want from your mobile application. On a food delivery app, people want to see available propositions, and discounts, place an order, track a delivery, and that’s it. They don’t want to read the history of a local family restaurant and its owners—this information may be kept in subsections. 

Accessibility 

If you want people to use your app, make it easy to reach. First, make sure it’s available on all mobile platforms and devices. Secondly, remember about inclusivity while designing for mobile devices. Partially blind or colorblind people may also use your product. So, you should help them effortlessly use it by adding voice interaction, and alternative text for images and employing color-blind oriented templates. Additionally, let customers change the text size in the app to suit their visual requirements.

Best Practices in Mobile App Design

Creating an interface that is both attractive and user-friendly is a challenge and an art. To achieve a delicate balance between aesthetics and functionality, designers need to utilize best practices. 

User Interface and Navigation 

Navigation in a mobile application must be intuitive. Each button or link has to state its purpose clearly. All icons must be recognizable and standardized. 

Mobile UX design's best practice for icons is to tag them with text. Tagged icons ensure that the purpose is conveyed quickly and easily.  You can see this in the most popular apps like YouTube, Spotify, and TikTok. 

Content Organization and Data Entry

When directing user attention across the application interface, use visual hierarchy. Distinguish important elements from less significant ones by varying size and spacing. Features that stand out attract attention, and clustering related items makes them easier to understand. For better readability and differentiation between titles, subheadings, and body content, designers should use the proper font sizes and styles. 

As for good examples of great content organization, we’d like to pay attention to Airbnb, an illustration of minimalism and simplicity. People love it for its aesthetic graphic design, balanced font sizes and colors, and the absence of flashy elements or bright banners. Airbnb has also made booking real estate clear: users understand what they are paying for.

Input fields and forms are the basic parts of mobile apps and are also crucial in mobile design principles. Improving the total user experience requires making these elements efficient and usable. Make a user type as little as possible by utilizing default values and input masks. Give consumers immediate feedback on validation so they can fix mistakes quickly. To help users fill out forms correctly, provide them with informative error warnings and visual clues.

Usability and Accessibility 

If you choose the right color palette, you can greatly improve the user experience. Remember about color blindness and test how your app looks to users with different types of color perception. Use contrasting colors for text and background to make content easy to read for anyone and anywhere. Add some extra explanations if needed. 

For example, the choice of clothing color in an online store should have a text explanation to be understood by people with a violation of color perception. Also, consider how the interface will look on small screens.

Performance and Responsiveness 

Take into account a diverse range of screen sizes and resolutions. Your design should be adaptive to ensure perfect display on any device. To check adaptability, test your design on a tablet, smartphone, and computer: this way you can adjust the display of elements in the interface and understand what and where to improve. Make sure the elements are not overlapping and are scaled correctly.

Platform Conformance

Research iOS and Android operating system guidelines. A guideline is a set of rules that determine how mobile apps are designed within an operating system. The iOS and Android systems have recognizable elements that users expect to see, so the guidelines help to create optimal UX and UI. Also, it will speed up the development process and reduce bugs.

Tools for Designing for Mobile Devices

As for the most popular tools designers use to implement solutions for mobile devices, we can consider the next four.

Figma 

Figma is an application for all kinds of graphical works: from creating website layouts to developing mobile interfaces and prototyping. Its biggest advantage is an opportunity to work directly in a browser, allowing access to projects from any computer or platform. You don’t have to buy multiple licenses or install the software. Another plus of the application is the collaboration feature that allows simultaneous changes to the project design by several users without the need to upload files locally.

Adobe XD

XD is designed from scratch to meet the requirements of modern UX/UI design with many features not available in other graphic applications. It solves the main problem that competitors can't cope with: it provides interaction with non-static elements and allows you to implement thoughtful dynamics on the page. Adobe XD is ideal for vector UI design, wireframe modeling, implementing engagement with interactive elements, and prototyping.

Sketch

Sketch is a simple vector tool. Designers and front-end developers use it to create UX/UI components. Sketch is simple and easy to learn, has an intuitive interface, and offers cross-platform design tools. It has a preview feature to see how the project will look on different devices. It has many good plugins and resources, plus some useful functions for working in the cloud.

InVision

InVision is a versatile design tool that focuses on maximizing UX for apps. It can be used to create automatically customized interactive prototypes for different devices. InVision Studio has tools for working with vector drawing, interactive design, and built-in animation. The application can be installed on macOS and Windows.

Mobile Application Design Process

UI UX mobile app design is a complex process that requires a thorough approach to ensure it is visually appealing and compatible with various devices and operating systems. You also need to make sure it’s handy for both experienced users and newcomers and meets modern trends in app design. So, what are the stages of mobile design?

1. Gathering data and design requirements. Research is a crucial part of the design effort, guiding the development of a product concept that accurately defines the market niche, functionality, monetization, and usage scenarios. 
2. Planning the architecture and navigation of the application. UX specialists create architecture based on application usage scenarios, ensuring navigation and consistency. This design stage is crucial for the end-user, as it directly affects the app's navigation and overall user experience.
3. Development of prototypes. Mockups are created for prototyping a mobile application, allowing for easy modification and identification of best solutions. This process involves discussing the architecture with the team and client, and later involving a team of developers to evaluate the technical design.
4. Testing the prototype. Interactive prototypes allow testing the app design to ensure it is suitable for the target audience, allowing for detailed feedback and understanding of user behavior patterns. This stage of development helps identify non-obvious patterns and improves the product's overall functionality.‍
5. Final refinements and development. Next, the agreed prototype is handed over to designers to make the product more attractive, refine the styling, and improve small user interactions, taking into account the final UI of a product. The final design is passed to developers who do the technical part.

Get Some Inspiration with Our Case Studies

Below, we invite you to check out some of our projects where we have successfully implemented all the ideas described above:

• Booking beauty appointment platform
• Takeout food delivery app
• Social network
• Advertising placement system
• Vehicle rental mobile app
• Smart city mobile app

Conclusion

Creating an aesthetically pleasing design is not enough. You also need to make it user-friendly and inclusive, bearing in mind mobile device demands.  Your target audience will be captivated and delighted by the appealing, highly engaging, and intuitive mobile applications if you carefully adhere to mobile app design principles and never stop learning from real-world examples of great mobile applications.

If your website has existed for a long time, this is a reason to think about redesigning it. The fact is that web development trends are constantly changing, and the things that attracted users around five years ago may seem high and dry today. 

In particular, several years ago, the independent web resource GoodFirms conducted a survey among designers to find the reasons why users leave their websites. It turns out that about 40% of respondents were sure that this was due to the outdated design. Do you need to look for more explanations as to why you need a redesign? If you don’t, this article is for you. 

What Is a Website Redesign?

Website redesign is a modernization that involves changes in the design, content, and functionality of a specific web resource. There may be not only a change in color scheme but also in the website’s structure, which also implies a certain optimization of the HTML code.

Redesign can be done every two years, or less often, every three or four years. However, when the appearance of your website remains the same for five years or more, your users are likely to begin to believe that your business is idle and, therefore, will likely choose a more successful competitor.

Why Is a Site Redesign Important?

Let's imagine that when you launched your website first, it generally satisfied its visitors. However, after a few years, its traffic began to gradually decline, although the quality of your service and/or goods did not decrease. Most likely, the problem is that your website’s design is outdated. Although this may seem to be the only reason why you need a redesign, there are several other reasons that also lead to it:

• You are going to migrate your website to a new engine;
• Despite regularly taking SEO measures, your website is not ranking well in search engines;
• You are planning to expand your audience reach and scale your business;
• Your company has rebranded, and now, this should apply to your website as well;
• You are entering a new market;
• Your product range or the concept of its promotion has completely changed.

Why You Need a Website Redesign Project Plan

In general, there are many goals that you can achieve through a redesign. The benefits of website redesign include improving the user experience, rebranding, increasing reputation, optimizing traffic indicators, conversions, etc., involving the website in marketing campaigns, and so on. 

At the same time, whatever goal you pursue, its achievement must be consistent, and each step must be justified from the point of view of business and available resources. Otherwise, you will act chaotically, and the result of these actions will most likely not live up to your desires. To prevent this from happening, you actually need a project plan for website redesign.

How to Redesign a Website: Applying Key Principles for Success

Now, it's time to find out what five sequential phases the correct redesign strategy includes. 

Setting Clear Objectives

Any well-thought-out project plan for website redesign implies matching goals and needs to redesign tasks to be completed.

Understanding the Need for Redesign

At the very beginning, you will need to correctly identify the reasons for the redesign. For example, this could be an outdated design, a not-mobile-friendly user experience, the functionality update and complicated website structure related to it, etc. You need to describe all these reasons as precisely as possible to make it easier for your team of designers (and possibly developers) to decide on the scope of work.

Defining Your Goals

Once the reasons are clear, you can define SMART (specific, measurable, achievable, relevant, and time-bound) goals. Special cases of such goals may be increasing traffic, achieving better conversion rates, improving user experience, etc.

Research and Preparation

At this stage, you will have to complete more complex tasks that will lay a solid foundation for your team's further work. They will also help you understand how competitive your website is at the moment and which aspects of it have the highest priority.  

Analyzing the Current Website

Your website is probably already connected to some analytics system where you can evaluate the dynamics of its traffic, conversion rates, abandoned carts, and so on. Thus, you have to collect this data, evaluate its changes for better or worse over the last year, and compare these changes with seasonal and local trends in your niche to get the most objective assessment of your website.

Competitor Analysis

You will also have to analyze the websites of your main competitors to understand in what they are good at and what needs optimization. Through this analysis, you will be able to prioritize the tasks that your technical team will have to deal with.

Target Audience Identification

Refine your audience data—this is especially important if you're looking to enter new markets or scale your online business. However, even if your goals are less ambitious, you'll still need to refresh information about your potential customers—their demographics, pain points, goals, needs, etc.

Content Audit

Finally, you should conduct a global content audit—for this, you'll probably need the help of SEO experts. Specifically, if your website's main problem is low search engine rankings, it's likely due to poor-quality content that will need to be optimized or replaced with a new one.

Planning and Strategy Development

Actually, you are left with the last steps before you hand over your website rebuild project to your technical team.

Site Structure and Navigation

Based on the key indicators of your website displayed in the analytics, you will be able to determine which of your website’s navigation elements and structure components need to be changed.

Visual Design Considerations

If your current website seems visually outdated, you will need to identify relevant examples from your competitors and indicate which graphical aspects of their websites you like the most.

Technical Requirements

If you find that your website's performance is suffering or, for example, the website itself is not fully displayed on some mobile devices, optimizing these aspects will require the participation of web developers.

SEO and Content Strategy

If the problem is the poor quality of the content (lack of necessary keywords in the texts, low-quality images, outdated data, etc.), you will need to involve SEO specialists in the project as well.

The Design and Development Process

Now, it’s the practical part of your website redesign roadmap, which requires the involvement of technical specialists and the designers themselves.

Wireframing and Prototyping

Now that the project is in the hands of the designers, they can start creating wireframes according to your requirements for the updated version of the website. As your requirements become more detailed, they will build prototypes based on these wireframes.

Design Mockups

An intermediate stage between the development of wireframes and prototypes can be the creation of mockups. Essentially, these are medium-fidelity sketches of your project that, like wireframes, schematically demonstrate the main components of the website but already have a pre-defined color palette and some graphic objects. Typically, this intermediate stage is necessary for websites with a complex and/or non-trivial structure.

Development and Coding

If your site requires migration to a new CMS or you have discovered problems in the previous stages that can only be solved through changes in its program code, you will have to involve developers to eliminate these bottlenecks.

Testing and Quality Assurance

And, of course, don’t forget to staff your project team with quality assurance specialists who will check if the updated version of your website works correctly before it goes public.

Launch and Post-Launch Strategy

Finally, you can begin preparing for the website relaunch.

Preparing for Launch

If your updated website looks the way you like and all the inconsistencies have been ironed out, it makes sense to check its effectiveness on the representatives of its target audience. This way, you may get additional insights about optimization that can still be implemented before your website’s launch.

Launching the Website

Now, developers can upload a new version of the website to the hosting. It may require you to choose a new hosting provider or a new tariff plan from the old one—this makes sense if you plan to expand the reach of your audience.

Post-Launch Activities

This stage will last exactly until the next redesign. In particular, you will have to monitor the analytics of your update to see whether the new key indicators meet your original business requirements. You will also need to constantly work on your website's SEO by regularly uploading new, relevant content.

Conclusion

We hope that in this article, we have helped you understand how to website revamp, and now, you can begin this procedure as consciously as possible. If you are looking for specialists ready to take on not only the planning task but also the implementation of your redesign project, feel free to contact us, and our team will bring your most ambitious goals to life within your budget and timeline.

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The year 2023 is when technology continues to overturn the existing order of things in the digital environment. Developers look for new ways to push development boundaries every year. From web development to machine learning, the languages we'll discuss today offer a wide range of possibilities and allow developers to turn their creative ideas into reality.

The most popular programming languages in 2023

Languages, once considered groundbreaking, are becoming the standard, while new versions are constantly emerging to meet the needs of the most demanding projects of all sizes. The rapid development of the digital world in all areas of business and industries leads to a constant demand for powerful programming languages that can be used to create innovative products that simplify the work processes of any business. And also increasing the level of efficiency through the automation of work processes.

In this dynamic programming landscape, some languages are rapidly gaining popularity because of their strengths and ability to meet modern developers' needs. At the same time, some traditional languages remain relevant and continue to play an essential role in the programming world. It is important not to lose the balance between "classics" and new languages that complement the development process and make it more straightforward.

We analyze the top programming languages shaping the technology landscape of 2023 and enabling developers to build high-performance applications, create intelligent algorithms and machine learning, develop web applications, and process massive amounts of data.

Whether you're a seasoned programmer or just starting out in the development world, these popular languages offer you the opportunity to explore new horizons in technology. Let's take a look at what programming languages are worth learning and using in 2023 to stay at the forefront of innovation and successfully implement your projects.

• Python continues to hold its position as one of the most popular programming languages. With its simple syntax, Python is ideal for developing web applications, data analysis, machine learning, and artificial intelligence. With an extensive ecosystem of libraries such as NumPy, Pandas, TensorFlow, and PyTorch, it provides powerful tools to solve diverse problems.

• JavaScript remains an integral part of web development and continues to hold an important place in the programming world. Because of its versatility and widespread use in frontend and backend development, JavaScript is a significant component for creating interactive user interfaces and web applications. Frameworks and libraries such as React, Angular, and Vue.js make developing JavaScript web applications effortless.

• Java is one of the most popular programming languages, especially in enterprise environments. Because of its reliability, portability, and extensive support from the developer community, Java is used to develop scalable applications, web services, and mobile applications on the Android platform. Java also offers a rich selection of frameworks, such as Spring, that simplify application development and management.

• TypeScript, a programming language built on top of JavaScript, will continue to gain popularity in 2023. It adds static typing to JavaScript, which helps developers identify errors at compile time and create more robust code. TypeScript is widely used to develop large projects and applications. Its support in popular integrated development environments (IDEs) such as Visual Studio Code makes it an attractive choice for many developers.

• Go (or Golang) is a relatively new programming language gaining popularity in 2023. Developed at Google, Go provides simplicity and efficiency, enabling the creation of high-performance applications and services. Because of its competitive performance and ability to scale quickly, Go is often used to create web servers, micro-services architecture, and big data processing.
  

Summary

These five programming languages are leading the development world this year. They provide developers with powerful tools for creating various applications and solutions, including web apps, mobile apps, and large-scale digital projects. The choice of programming language depends on the specific needs of the project and the developer's preferences. Still, these five leaders provide a solid foundation for success in today's technology environment.

The technological landscape is constantly changing, and programming languages are also evolving. New languages emerge, and existing ones are updated and adapted to changing needs. Therefore, developers need to remain flexible, adapt to the rapidly changing technological environment, keep abreast of the latest trends, and learn new technologies and programming languages to successfully implement projects of any complexity.

If you are working on a digital solution, one of its integral stages will be UX/UI design. While trivial projects usually have “beaten” paths for creating navigation and visuals, any deviation from the standard can lead to dissatisfaction among end users and increase the entry threshold for them. In this regard, no matter how sophisticated the logic of your project is, it is important to understand the usability principles maintained by human psychology to ensure its excellent user experience.

Theories of Perception in UX/UI Design

Generally speaking, there are several popular theories of perception in UX/UI design:

• Gestalt psychology aimed at gaining an understanding of how people associate individual objects with things that are meaningful to them. The main gestalt principles include figure-ground (according to which some objects, depending on their visual appeal, are perceived as key navigation elements, while others serve as background), similarity (according to which elements with identical visual characteristics are perceived as interconnected), proximity (according to which closely placed objects are also perceived as related to each other), and closure (which states that incomplete objects, if sufficiently detailed, can be perceived by users as complete).
• Affordance theory, claiming that to ensure intuitiveness, users should receive some cues from the interface from the very beginning on how to deal with a particular element—one way to do this is to use skeuomorphism;
• Hick's Law, which states that the time it takes a particular user to make a decision is logarithmic to the number of available options (which means that to maximize intuitiveness, the user's choice should always be limited);
• Fitts' Law, stating that the time it takes a user to reach a desired part of the interface depends on its size and distance (which means that to ensure a great user experience, you need to place interactive elements closer to the user's starting point);
• Signal Detection Theory, claiming that users can subconsciously distinguish between meaningful cues (calls to action) and background noise, meaning that they should be visually separated.

Cognitive Load: Its Effects on UI Design

Now let's move from theory to practice and consider individual insights extracted from the above theories of psychology in UX. 

In particular, the first insight is the minimization of cognitive load, that is, the efforts of the human brain that must be applied to perform a specific target action. Thus, the importance of reducing the amount of information or tasks provided to the user becomes clear—otherwise, there is a high probability that the user will begin to act erroneously. 

In practice, such brevity can be achieved by correctly grouping interrelated elements and removing elements whose functionality duplicates each other.

Emotional Response & UX Design

Another valuable insight from UX design and psychology is to establish an emotional connection with the end user through your interface. This is a global task that begins with user behavior analysis and identifying their desires, pain points, and intentions and ends with transforming the obtained information into design elements that resonate with them. 

Ultimately, you will have to walk in the shoes of the average consumer of your software solution to ensure the proper level of empathy with your emotional design. In terms of practical implementation of the emotional response, such user empathy can be achieved through the integration of “user experience stories” that can be presented by a service provider to users as they get acquainted with the digital solution, as well as the use of the right color palette, forms, audio, pop-ups, and text.

The Role of Memory in User Interaction

Since, according to cognitive psychology in design, the average person can concentrate on one object for up to 20-30 minutes, it is important to take this aspect into account if your software solution involves long-term interaction with it. 

Specifically, you can reduce the load on your users' memory by implementing elements and scenarios that work and occur in a way that is familiar to them. Otherwise, if something does not function as expected, it can lead to users’ confusion and erroneous actions. 

Understanding User Behavior Through Heuristics

Heuristics are specific UI design principles that can predict user psychology. Here are some of them:

• Visibility of system status, which implies the importance of informing end users about what is happening with your software solution at the moment through progress bars, loading animations, or status messages;
• Match between the system and the real world, which emphasizes the importance of correspondence between the system's behavior and the knowledge and experience that users received before they became familiar with your software solution;
• User control and freedom, which implies the need to provide end users with the ability to freely navigate your software solution and manage negative scenarios;
• Consistency and standards, which determine the significance of logical connections between elements, terminology, and layouts;
• Error prevention, which consists of preventing erroneous actions by end users by providing them with unambiguous and understandable instructions;
• Recognition over recall, which states that to ensure better intuitiveness, users should “recognize” familiar patterns from the real world in elements and scenarios of your interface.

Thus, you have to perform a heuristic evaluation to check whether your solution is good in terms of usability. Also, note that in reality, there are many more psychology UI design heuristics, but the above list will be sufficient for a basic understanding of what is meant by the term excellent user experience.

The Impact of Attention on User Experience

Many designers, in order to encourage the user to perform a particular action, abuse pop-up notifications and other features to attract attention, thereby increasing the cognitive load, the inadmissibility of which we have already discussed earlier. 

Instead, you can use such approaches as the correct building of a visual hierarchy, optimization of the color palette, breaking down complex processes into several simple ones, etc. And, of course, do not forget to appeal to the memory of users, which already contains some experience of interaction with other digital solutions and can automate some processes of dealing with them due to the familiarity of the mental models implemented.

User Motivation & Reward Systems in UX/UI Design

Finally, from the point of view of UX psychology, many UX/UI designs benefit from gamification, which motivates users to move toward the target action. In general, gamification is aimed at boosting user engagement and creating an exciting user experience that they don’t want to interrupt. Its main elements are rewards and recognition.

At the same time, it is crucial to note that some reward systems are too confusing for the average user and, thus, create unnecessary cognitive load instead of motivation. Moreover, gamification is not considered a universal approach that could be used in any project. Therefore, you have to always start with the appropriateness and the affordance of implementing this concept clearly and simply for the consumer.

Conclusion

We hope that with the help of this article, you were able to understand the importance of UX design psychology and how it is implemented in UI/UX principles like heuristics, emotional response, lowering cognitive load, user motivation, and others. If you are interested in creating a digital solution that fully meets all these psychological principles, feel free to contact us.

Statistics claim there are 8,945 programming languages, yet we bet neither developers nor businesses need all of them to get market traction. As we enter Q1 of 2025, the programming world hasn't changed much; almost every programming languages rating is still shaped by a mix of established languages and modernized classics like Java and Python. However, it has shifted to include innovative tools like AI, blockchain, data science, and IoT. 

To cut through the clutter, we've analyzed the latest trends and compiled a list of the top 10 programming languages for 2025, an overview of different rankings, and the best ideas on choosing a programming language for a project. Read on for success.

Programming Languages Ratings

As of early 2025, some programming language rankings are still based on 2024 data. However, developers, businesses, and educators can leverage PYPL, TIOBE, and GitHub insights to guide learning efforts, hiring strategies, and project development. 

Read all the ranking lists and find the most desired programming language in 2025.

PYPL Ranking 2025

The PopularitY of Programming Language (PYPL) index is a Google-powered snapshot of programming language popularity. It tracks tutorial searches, giving a clear view of which skills are in demand—a crucial tool for tech trend analysis.

As per the PYPL report, 5 five most popular programming languages are: 

The TIOBE Ranking 2025

The TIOBE Index provides a monthly snapshot of programming language popularity. It ranks languages by global developer presence, educational material availability, and industry adoption based on search trends from platforms like Google, YouTube, and Wikipedia. Amazon and Baidu.

According to TIOBE, the five most popular programming languages are:

The GitHub Ranking 2025

GitHub is a global code hub shaped by Fortune 50 companies. While GitHub doesn't provide rankings, tools like GitRanking organize repositories, and DevRank measures developer influence. Here are the dynamics of popularity from the latest GitHub report:

IEEE Spectrum Ranking 2025

From GitHub commits to Stack Overflow threads, this annual roundup analysis cuts through the noise of top programming languages with three crystal-clear metrics: established giants, breakthrough performers, and market demand. Here is the latest technical ranking IEEE report:

RedMonk Ranking 2025

RedMonk ranks programming languages twice a year, blending GitHub code usage with Stack Overflow engagement. They track adoption trends and developer preferences by combining code usage with developer discussions. Over 45% of software companies use these quarterly rankings to guide their technology decisions.

Here are the latest insights from their programming language RedMonk rankings:

Stack Overflow Developer Survey 2025

The Stack Overflow Developer Survey collects data from over 30,000 developers to explore their opinions and habits. It highlights trends in technology usage and preferences, illustrating how new tools and frameworks rise in popularity each year while others fade away.

According to this survey, the five most popular programming languages here are:

What Affects Programming Language Rankings?

Language rankings depend on GitHub stats, Stack Overflow activity, and job demand. Tech giants can elevate a language overnight. Community support ensures good documentation, while technical strengths drive relevance in AI, cloud, and mobile. Overall, enterprise adoption hinges on commercial investment in tools and frameworks.

Why Do Developers Switch Languages?

Market demand and salary potential often trigger language switches. New projects at work may require learning unfamiliar stacks. Some developers migrate when they hit technical walls with their current language. Others switch purely out of curiosity to explore fresh programming concepts. Better developer experience—from cleaner syntax to powerful debugging tools—can seal the deal.

How Do Trends Impact Programming Language Rating?

Tech trends reshape the programming landscape constantly. Cloud computing favors languages that handle concurrency well. Mobile development boosts cross-platform-friendly languages. AI and ML create demand for specialized numerical computing capabilities. Modern software architecture—like microservices and serverless—influences language choice based on performance needs. Open source momentum can make or break a language's trajectory. Universities follow these shifts, molding new developers' skills accordingly.

Why Do Some Languages Rise or Fall in Popularity?

Languages surge when they solve pressing problems—like Rust addressing memory safety. Corporate champions (Google with Go, Microsoft with TypeScript) accelerate adoption through resources and integration. Growing ecosystems create network effects, attracting more developers and tools. Languages decline when they stagnate or lose their edge to newer alternatives. Yet some persist through enterprise inertia, particularly in legacy systems. Success hinges on balancing innovation with stability while nurturing a vibrant developer community.

Top 10 Most Popular Programming Languages in 2025 

From crunching data and financial management apps to renewable energy systems, optimizing solar panel efficiency, and reducing carbon footprints—programming spans different fields of humankind. 

But here's the million-dollar question: with so many programming languages out there, which ones are worth learning? We turn to the database of the most desired and loved programming languages from Stack Over Flow. Here is the list of the top 10 below:

1. Python

With clean syntax and broad applications, Python is a go-to language for web development, AI, and data science. Supports procedural, object-oriented, and functional programming, consistently placing it at the top of programming language rankings.

Pros:

• Easy to Learn: Simple syntax makes it beginner-friendly.
• Extensive Libraries: Large collection of standards and toolkits available.
• Integration: Works well with languages like C and C++.
• High Demand: Especially among startups.

‍Cons:

• Slower Speed: Slower compared to compiled languages.
• Limited Concurrency: Global Interpreter Lock (GIL) limits true parallelism.
• High Memory Use: Consumes more memory than some alternatives.
• Complex Debugging: Dynamic typing can complicate the debugging process.
• Poor for Certain Applications: Less suitable for mobile, embedded, and real-time systems due to efficiency concerns.

Common Use Cases of Python:

Thanks to TensorFlow, Python excels in data science and web development, particularly in AI and ML. From Instagram to Dropbox, it powers a wide range of applications. We bet that in years ahead, we'll still see Python as the number №1 solution, ranked in most ratings.

2. JavaScript

Among all the most popular programming languages, JavaScript remains the "default" one for web developers. Born for the browser, JavaScript now dominates both frontend and backend, thanks to Node.js and a massive npm ecosystem. In 2025, it will continue to dominate as a leader on GitHub in terms of code volume.

Pros:

• Versatile & Rich Ecosystem: Works for front-end, back-end (Node.js), and mobile, with a vast array of frameworks and libraries (React, Angular, Vue).
• Client-Side Responsiveness: Executes directly in the browser, reducing server load and improving user experience.
• Interactive Web Pages: Enables dynamic features, animations, and real-time updates.
• Large, Active, and Supportive Community: Ensures ample resources and solutions.

Cons:

• Security Vulnerabilities: Client-side code is visible and susceptible to attacks (XSS).
• Inconsistent Browser Behavior: Code may render differently across browsers.
• Performance Limitations: Heavy client-side JavaScript can impact loading times.
• Debugging Complexity: Client-side debugging can be challenging.
• Single-Threaded Constraints: Limits performance for complex, concurrent operations.

Common Use Cases of JavaScript:

JavaScript powers web development with React, Angular, and Vue.js for interactive interfaces and single-page apps. Node.js extends its reach to server-side development, real-time apps, and microservices.

3. TypeScript

TypeScript is a Microsoft-backed superset of JavaScript that adds static typing and class-based object-oriented programming. It compiles to JavaScript and runs anywhere JavaScript does. Strong type safety and advanced tooling make it a top choice for large-scale development. In 2025, it will remain in the first position of top programming languages.

Pros:

• Type Safety: Static typing helps catch errors during development rather than runtime.
• Enhanced IDE Support: Better code completion, refactoring, and error detection in development tools.
• JavaScript Compatibility: Can use all JavaScript libraries while adding type safety.
• Enterprise-Ready: Perfect for large codebases and team collaboration.
• Improved Code Quality: By catching errors early, TypeScript reduces the likelihood of bugs in production.
• Better Collaboration: Type annotations make code more self-documenting, making it easier for teams to understand and work on the same codebase.
• Scalability: TypeScript is particularly well-suited for large and complex applications.

Cons:

• Type Definition Files: Some libraries may lack or have outdated type definitions.
• Complexity: Initial installation and setup might be a bit tricky.
• Development Time: Writing types can slow down initial development speed.
• Compilation Step: TypeScript code needs to be compiled into JavaScript before it can be executed.
• Increased Code Size: Type annotations can increase the size of your code.
• Runtime vs Compile time: while Typescript catches many errors at compile time, it does not guarantee that there will be no errors at runtime.

Common Use Cases of TypeScript:

It's the default for Angular and powers tools like Visual Studio Code and Slack. Its compatibility with Node.js makes it a top choice for full-stack development, prioritizing type safety.

4. Rust 

Rust, developed by Mozilla Research, delivers memory safety, high speed, and a growing ecosystem. Its ownership model eliminates memory vulnerabilities without a garbage collector. Adopted by startups and major tech firms, Rust is a rising star that won't stop raising its popularity for performance-critical development.

Pros:

• Memory Safety: Catches errors before runtime, not during production crashes.
• Performance: Matches C/C++ speeds with higher-level abstractions.
• Cargo: A modern package manager that just works. 
• Cross-Platform: Write once, deploy anywhere approach.
• WebAssembly Ready: First-class support for the web's low-level powerhouse.

Cons:

• Learning Cliff: Ownership and lifetimes concepts demand serious study.
• Unforgiving Compiler: The "fighting the borrow checker" experience is real.
• Build Times: Complex codebases can test your patience during compilation.
• Young Ecosystem: Some specialized tools are missing in the standard of mature languages.

‍Common Use Cases of Rust:

Rust shines in critical infrastructure: Firefox's core engine, Discord's real-time systems, and cutting-edge blockchain platforms. It's the go-to choice for projects where failure isn't an option—embedded systems, operating system kernels, and game engines pushing performance boundaries.

5. Go (Golang)

Go, developed by Google, is a high-performance language known for simplicity, efficiency, and scalability. In 2025, Go continues to rise with native scaling and a lightweight yet productive design, often chosen over Java or C# for backend and DevOps. While Rust challenges C++, Go is a streamlined alternative for modern software teams. Consequently, in recent years, it frequently tops each rating of programming languages.

Pros: 

• Simplicity: Clean and straightforward syntax, easy to learn. 
• Concurrency: Excellent built-in support for goroutines and channels. 
• Performance: Fast compilation and execution speeds, comparable to C++. 
• Garbage Collection: Automatic memory management, reducing the risk of memory leaks. 
• Static Typing: Catches type-related errors at compile time. 
• Standard Library: Comprehensive and well-documented standard library. 
• Cross-Compilation: Easy to compile for different platforms. 

Cons: 

• Lack of Generics: Limited code reuse for different data types (now addressed but still evolving). 
• Verbose Error Handling: Requires explicit error checking, which can lead to verbose code. 
• Limited Expressiveness: Some developers find the language less expressive than others. Dependency Management: Older dependency management was considered cumbersome (now improved with modules). 
• Smaller Ecosystem: While growing, the ecosystem is smaller than more established languages.

‍Common Use Cases of Go:

Go is widely used in cloud-native development, web development (server-side), and network programming. It's popular for building microservices, distributed systems, and command-line tools. Companies like Google, Docker, and Kubernetes utilize Go for their infrastructure and tooling.

6. C#

C# is a modern, object-oriented coding language developed by Microsoft as part of the .NET platform. It blends the robustness of C++ with the simplicity of Visual Basic, featuring strong type safety, versioning, and scalability support. In 2025, C# will remain the strongest language in the Microsoft ecosystem and seems not to lose its popularity.

Pros:

• Type Safety: A strong static typing system prevents common runtime errors and improves code reliability.
• Rich Ecosystem: Extensive standard library and NuGet package ecosystem for nearly any functionality.
• Cross-Platform: .NET Core/.NET 5+ enables true cross-platform development and deployment.
• Performance: High-performance capabilities, especially with recent memory management and compilation improvements.
• Modern Features: Pattern matching, nullable reference types, LINQ, and continuous language evolution.

Cons:

• Platform Dependencies: While cross-platform, some libraries still have Windows dependencies.
• Resource Usage: Higher memory footprint compared to lower-level languages.
• Compilation Required: Must be compiled before execution, adding a build step.
• Learning Curve: Complex features and patterns can be challenging for beginners.
• Verbose Syntax: More ceremonial code compared to dynamic languages.

Common Use Cases of C#:

C# is widely used in enterprise software development, particularly in the Microsoft ecosystem. It powers everything from ASP.NET web applications to Unity game development, Windows desktop applications, and cloud services on Azure. 

7. C/C++

C and C++ pioneers of modern computing remain unmatched in hardware control. C provides procedural efficiency while C++ adds object-oriented features; both deliver raw performance critical for systems development.  In 2025, it remains first among the 10 top programming languages.

Pros:

• Zero-Overhead Abstraction: Efficient execution with direct hardware access.
• Precise Memory Control: Fine-grained resource management without a garbage collector.
• Cross-Platform Compatibility: Runs on diverse architectures and operating systems.
• Extensive Legacy Codebase: Decades of existing libraries and software.
• Deep System Integration: Ideal for OS development, embedded systems, and performance-critical applications.

Cons:

• Manual Memory Management: Prone to leaks and undefined behavior.
• Complex Syntax: Challenging constructs like C++ templates.
• Slow Compilation: Long build times for large projects.
• Security Risks: More exposure to vulnerabilities like buffer overflows.
• Steep Learning Curve: Requires deep understanding of low-level concepts.

Common Use Cases of C/C++:

These languages drive OS development, game engines, embedded systems, real-time applications, and high-performance computing, including Chrome's V8 and major databases.

8. R

R is an open-source language for predictive analytics and data visualization, built with C, C++, Fortran, and R. It features S3 and S4 frameworks for flexible data modeling and method dispatching, which allows for flexible data modeling and method dispatching.

Pros:

• Statistical Analysis: Built-in capabilities.
• Visualizations: Publication-ready.
• Packages: Extensive scientific libraries.
• Math Operations: Advanced support.
• Community Support: Favorite among researchers.

Cons:

• Big Data Performance: Struggles with big data performance.
• Memory Usage: Can spiral quickly.
• Syntax Complexity: Unique syntax challenges newcomers.
• Package Quality Variance: Package quality varies wildly.
• Limited Use Cases: Beyond data analysis.

Common Use Cases of R: 

R dominates biotech labs and research institutions. Companies like Pfizer and Merck crunch clinical trial data while hedge funds model risk. Academia relies heavily on R for peer-reviewed research and statistical validation.

9. Java

Java is a high-level, object-oriented language known for its platform independence. With ongoing improvements, including syntactic enhancements and advancements in GraalVM and WebAssembly, Java is expanding beyond traditional backend development to stay competitive.

In 2025, Java will steadily retain its leadership in the top 10 programming languages as a workhorse for enterprises and large high-load systems. If you plan to work with large corporations, the banking sector, or any system that requires stability, Java is an excellent choice.

Pros:

• Platform Independence: Code runs anywhere via the JVM.
• Rich Ecosystem: Extensive libraries and frameworks like Spring and Hibernate.
• Concurrency Support: Built-in multithreading enhances performance.
• Security Features: Strong protections make it ideal for web and enterprise applications.
• Community Support: A large user base ensures abundant resources and tools.

‍Cons:

• Verbose Syntax: Requires more code compared to modern languages like Python.
• Performance Overhead: Slower than lower-level languages due to JVM interpretation.
• Memory Intensive: Consumes more memory than C or C++.

Common Use Cases of Java: 

Java is everywhere—from Google's backend to Fortune 500 banks. It's vital for Android, Spring, and high-load systems, and with cloud innovations, it continues shaping enterprise tech, big data, and finance.

10. PHP

Once a simple scripting language, PHP now drives much of the web, including WordPress and Facebook. Modern frameworks like Laravel have refined it, making PHP more efficient and scalable than ever.

‍Pros:

• Web Integration: Excellent integration with HTML and web servers.
• Easy Deployment: Simple setup and hosting requirements.
• Large Community: Extensive documentation and community support.
• Framework Ecosystem: Modern frameworks like Laravel provide robust development tools.
• Cost-Effective: Wide availability of hosting and developers.

Cons:

• Inconsistent Design: Historical inconsistencies in function naming and parameter ordering.
• Security Concerns: Easier to write insecure code if not following best practices.
• Type System: Weak typing can lead to subtle bugs (though improving with recent versions).
• Performance: Not as performant as compiled languages for complex applications.
• Legacy Code: Many older codebases use outdated practices.

Common Use Cases of PHP: 

PHP continues to dominate CMS, e-commerce, and traditional web development. Ideal for small to mid-sized projects, it remains the backbone of WordPress, Wikipedia, and countless content-driven platforms.

How to Choose a Programming Language for a Project?

While language wars rage on Twitter, programming languages ratings "hustle-and-bustle with numbers," successful tech leads know it's not about which language is "best" – it's about which one serves your specific mission. Let's delve into tips on how to figure out what programming language suits your business best.

Project Type

• Identify the target platform (e.g., web, mobile, desktop, or multi-platform).
• Define the application's purpose and user environment.

Language Features

• Evaluate performance, functionality, and built-in capabilities.
• Assess support for modern coding paradigms (e.g., functional, object-oriented).

Ecosystem

• Explore the availability of tools, plugins, and development environments.
• Investigate the breadth of open-source contributions and ecosystem maturity.

Scalability

• Analyze how well the language supports large-scale applications.
• Consider its ability to handle increased user loads or data growth efficiently.

Learning Curve

• Determine how intuitive the language is for new developers.
• Assess the time required to become proficient compared to alternatives.

Compatibility

• Verify how seamlessly it integrates with existing systems and APIs.
• Check for cross-platform support and interoperability with other technologies.

Industry Demand

• Research hiring trends and industries actively using the language.
• Examine its prevalence in job postings and developer surveys.

Support

• Review the quality and availability of official documentation.
• Look into active communities, forums, and professional support channels.

Future Trends

• Monitor the language's adoption of cutting-edge technologies (e.g., AI, blockchain).
• Analyze its trajectory in developer rankings and long-term viability.

Here is a detailed overview of the area domain and programming language features and connections:

Don't Fall Behind With the Wrong Programming Language

In most cases, when it comes to real development projects—it's not just about code but performance, scalability, and future growth. Checking the most popular programming language ratings ensures a wise, future-proof decision. Choose a language that fits your needs, leverages your team's expertise, and keeps you ahead of the curve. With Darly Solutions, you're always one step ahead.

Backed by deep industry knowledge and hands-on experience, we help businesses navigate the ever-changing tech landscape. Whether launching a startup or scaling an enterprise, don't let the wrong decisions hold you back—let's build for the future together.

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.

The mobile apps market is rapidly growing. According to Grand View Research, the global mobile app market was evaluated at approximately USD 253 billion in 2023 and is going to grow with a 14.3% CAGR by 2030. That’s why the need for qualified mobile development specialists is so relevant today. 

At the same time, one of the key features of a competent specialist is knowledge of the software development life cycle (SDLC) and which of the existing models is best to use in a specific project. The following article will overview the most popular SDLC models and explain where to apply them for the best results. 

What is the Software Development Life Cycle (SDLC)?

The software development lifecycle is a methodology that guides the development team during the app creation process. Each of the development life cycle phases has its aims, timeframes, and challenges. Yet, a systemized approach toward software development ensures that the final product is of the highest quality. 

In comparison, there are other approaches to software development. For example, development teams can choose from the following options: 

• Application lifecycle management. It has a broader scope outside of the actual development process. It can include several SLDC development life cycle stages inside. 
• Software testing lifecycle. This method emphasizes debugging and elimination of possible errors in the product. 
• Product development lifecycle. This strategy includes market and product promotion after its development is complete. 

What Is the Purpose of the Software Development Life Cycle (SDLC)?

The SDLC’s clear division into development cycle stages ensures that software is developed in a controlled environment. The systematic approach helps to avoid such challenges as: 

• Budget breaches.
• Project overruns and crunches.
• Failure to meet the requirements.
• Expired deadlines.

Creative freedom is important during the software development process. Yet, the SDLC approach ensures that all the creativity and revolutionary solutions are channeled constructively. 

While following the life cycle of software development, each team member knows what task they should do and its deadline. 

What are the 7 Phases of SDLC? 

There are seven common SDLC phases: 

1. Requirements analysis 
2. Planning
3. Design 
4. Development
5. Testing
6. Deployment
7. Maintenance

Let’s consider them in more detail.

Requirements Analysis 

It is the initial stage at which the developer's team evaluates the required features for the app. It defines how long the app will be in development, the budget’s size, and the deadlines. 

This step answers such questions as: 

• What is the objective of the app? 
• Is it an upgrade or a brand-new software? 
• What are the expected results? 

Planning 

The project can be planned after its goals and desired results are established. Planning in SDLC defines the technological stack and project roles. The project roadmap is also developed during the planning phase of SDLC. 

Design 

The software blueprint is created during the design stage. Ideas turn into features, architecture, and UI/UX design. Designing also includes consideration of the software's platforms and security as a part of SDLC processes. 

Design can undergo several iterations before reaching its final look. During this phase, teams usually create a software prototype to see how all the features work together. This phase concludes with design document specification—a text description of the app’s functionality to share with stakeholders and within the team. 

Development 

Development is the long-lasting of the SDLC steps. During this stage, the team implements planned features and functionality into actual software. Tools for progress tracking and version control, such as Git, are common in the software development stage.

Testing 

Testing comes after the implementation phase in SDLC. It is a rigorous process during which all software elements are evaluated. Testing can include: 

• Performance testing.
• Security testing.
• Integration testing.
• Alpha and beta testing, and so on.

The result of the testing and debugging software development cycle phases should be a product meets its specifications and is ready for the commercial environment. 

Deployment 

Usually, the apps undergo testing and development in a secluded, special environment. Deployment implies transferring the software to the commercial space. The deployment phase includes final testing of the working product and gathering feedback from a limited audience. 

The product is ready for full-fledged commercial use when the software development stages are over. 

Maintenance 

Maintenance is the final stage of the SDLC cycle. It includes software updates, security, and performance optimization. An upgrade or upscale of the software may be required in the future. In this case, the SDLC begins from the first stage, and the major update undergoes all the steps mentioned. 

Most Common SDLC Models 

The SDLC standards are common across the industry and development teams. However, there are different SDLC models. Each model serves its own purpose and offers a different order in which the different phases are performed.

SDLC Agile Model 

The Agile SDLC model is the hallmark of modern industry. This model implies dividing the bigger tasks into smaller chunks and working on their implementation. The Agile method emphasizes communication between cross-functional teams and stakeholders. 

Agile also implies frequent feedback gathering and analysis. Rigorous quality control is also an integral part of the Agile model. 

SDLC Big Bang Model

The Big Bang model is an almost informal approach to the SDLC cycle. It ignores any planning or assessment and jumps straight into the coding. Such an approach is more risky than others. However, the model works best if delivery time is critical. 

The Big Bang model can work for you if you wish to experiment or test waters in new markets. 

SDLC DevOps Model

DevOps is a combination of development (Dev) and operations (Ops) teams to streamline the delivery of software products. The combined practices require service providers of highly skilled specialists. The main DevOps principles are continuous integration and delivery at the enterprise software development level. These methods help automate the process and streamline management tasks. 

The DevOps model can strain the budget. However, the result is worth the cost. The DevOps SDLC cycle ensures that the product is in a deployable state and can be swiftly modified. 

SDLC Iterative Model

An Iterative model is another representative SDLC cycle. Unlike the Agile model, iterative SDLC does not require continuous customer involvement. Besides, the iterative model has a more defined structure. 

The Iterative model implies iterating the software and making changes between the cycles. Such an approach works well for expanded teams and allows the desired results to be achieved without failing deadlines or breaching the budget. 

SDLC Lean Model

The Lean model is one of the oldest SDLC cycles that has persisted until today. It aims to achieve maximum efficiency in resource management. Unlike Agile, it focuses on the highest possible quality instead of close and consistent communication with the client. 

It also takes a broader than Iterative model approach and improves the overall development process rather than the product itself. Lean is more philosophy than a precise model. As an SDLC model, Lean will ensure the quality of the product and client satisfaction before everything else. 

SDLC Spiral Model

The Spiral model is a combination of Iterative and Waterfall models (this one will be discussed below), taking the best features from both approaches. The model consists of spirals, and each spiral has the following steps: 

• Planning 
• Risk analysis 
• Development 
• Testing

Such an approach allows risk mitigation at the early stage of the project. While being time-consuming, the Spiral model can be a fail-proof net for your project. 

SDLC Waterfall Model

If you are looking for something straightforward and easy to manage—the Waterfall model is your choice. It takes the SDLC structure at face value and follows each step consecutively. The Waterfall model has its risks. It is rigid, and if changes or feedback appear in later stages, it will be hard to implement them. At the same time, the Waterfall model struggles with scalability. 

On the other hand, it can spare the headache of complex management and task delegation. 

Conclusion 

Software development is a complex process that requires structure and precision. The Software Development Lifecycle provides such a structure. In seven steps, a software development team can clearly see goals and meet the client’s requirements. 

Various SDLC models fit different needs and project scopes, and we know how to work with all of them. If you need specialists for custom software solutions, we are ready to answer your call and create a high-quality product. Contact us to discuss your project in detail.

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 have a great idea, and it involves creating something innovative, aiming at solving standard problems using unconventional methods and tools, it’s probably worth bringing it to life gradually. This way, you will move from priority features to secondary ones, relying on real users’ opinions—both positive and negative ones. Actually, here, we described the essence of the MVP development process. Below, we will explain its definition and check its benefits and specifics of implementation. 

Understanding MVP in Software Development

If you are going to build an MVP, you should first understand exactly what it is.

In a nutshell, a minimum viable product (MVP) is a product that has basic functionality to satisfy some needs of the target audience. Its main goal is to attract first users, determine their impression of interacting with it, and think through a further strategy for its development.

Key advantages of an MVP approach

When you decide to build MVP, you get the following benefits:

• Ability to confirm or refute hypotheses about the product
• Attracting investors through accelerated confirmation of the idea’s viability
• Help in analyzing the target audience
• Reduction of financial risks
• Development costs reduction thanks to proper prioritization of the product’s features
• Faster engagement of the target audience, long before the launch of a finalized product version
• Early bug fixing
• Reduced time to market

Thus, by resorting to MVP building, you can start to earn faster. At the same time, the development of a minimum marketable product is especially relevant for startups as it allows you to find out the real needs and pain points of your target audience in practice.

Developing Your MVP: Focus on Core Functionality

Now, let's find out what the MVP process looks like.

Identify your target audience

No matter how universal your solution is, its audience still has something in common, be it goals, pain points, demographics, or something else. Therefore, before you hone your product concept, you will have to define who it will be intended for—that is, conduct user research. To do this, you need to draw up a detailed portrait of the client—gender, age, place of work, hobbies, income level, desires, needs, problems, etc.

Come up with core functionalities

Surely, you have already seen a picture on the Internet that reflects the essence of MVP—a skateboard gradually turning into a car. Formally, this means that you need to understand which functions will be fundamental, that is, without which no user will be able to implement the task assigned to your product. These will be your MVP requirements. Then, as you receive reviews from real users, you can add features of less priority that make your solution more usable than the previous version.

Prioritize correctly

Before you start to develop MVP, think about which stages of your project are the most resource-intensive—they will need to be implemented first. As resources become available, you can assign lower-priority tasks to individual members of your team. Actually, this prioritization will determine your product roadmap. Thanks to this approach, you will eliminate downtime in your work processes and will be able to bring the MVP software launch date closer.

Define tools and resources

Finally, you should determine what tools you will use in development and what resources will be needed for this. We are talking about programming languages, wireframing and software development tools, platforms for project management, business messengers, etc.

Iterating Based on User Feedback

Any MVP strategy consists of iterations—that is, constant repetition of the development cycle based on user feedback received after testing its previous version. Let's look at the nuances of implementing these iterations.

Data collection methods

Depending on the resources available, you can collect data from real users by applying different methods. This could be live interviews, questionnaires, A/B product testing, supervised and unsupervised testing, and so on.

Feedback analysis and prioritization

Once the data has been collected, you will need to identify which reviews should be processed first and which ones can be postponed. In fact, everything is simple: the highest priority belongs to those that indicate that the user cannot perform the target action in your solution at all. Second in priority are reviews related to the complexity of the functionality—that is, those in which users claim that they did not immediately figure out what to click, or that they had to spend too much time to perform a simple operation. Everything else that does not affect the accessibility and simplicity of functionality can be optimized later, in subsequent updates.

The importance of iteration

Since building MVP for startups involves creating a primitive version of a full-scale solution, most likely, you will have to deal with a lot of negative feedback. Some will not like the typography, some will find the navigation inconvenient, and some will not even understand what actions need to be performed to achieve the goal. This is okay. At the same time, those who leave you reviews now are likely to use your solution after its optimization, too—therefore, in order not to lose their loyalty, you should constantly improve it. This will show your audience that you appreciate their opinion and that they also contribute to your project.

Launching Your MVP: Getting It Out There

Each iteration that brings your MVP closer to its finalized version must fulfill a specific business task—this could be confirming or refuting hypotheses, testing functionality, attracting users, choosing the best version from several proposed ones, identifying competitive advantage, etc.

This means that with each new iteration, you may have to take different approaches to each stage of the development cycle, from MVP planning, as well as choosing development tools and startup methodology, to testing and determining methods for collecting user feedback. Ultimately, this flexibility will open up further development prospects for your minimum viable prototype and reduce the risks of its possible failure.

Conclusion

Generally speaking, the benefits of creating an MVP from a business point of view cannot be overestimated. This approach helps reduce the risks of failure, attract investors, ensure a more moderate expenditure of the project budget, evaluate its competitiveness, and much more. In addition, it allows you to quickly enter the market because instead of the time-consuming development, you can spend less time creating its primitive version and, thus, more quickly test its viability and prospects in practice.

If you are just considering the possibility of implementing your business idea in MVP format, feel free to contact us. We will take on all stages of its implementation, from concept approval to usability testing and launch, and will repeat iterations until it transforms into a full-fledged product.

With more than 2.7 billion smartphone users across the world in 2020, it’s no surprise that the mobile app industry is expended with no signs of stopping. Nowadays, smartphones have become a useful and convenient instrument that offers multiple benefits such as communication through emails and social networking, web surfing, entertainment, GPS and various other productivity apps.

In fact, studies show that the average American checks their phone every 12 minutes. 10% of people check their phones every four minutes.

We use our phones every day at work, at home, on the street, in bed, while we are eating, in our cars. You might be reading this article from a mobile device right now.

What are people doing on their phones? Well, 90% of mobile time is spent on apps.

According to these facts, many companies considering mobile apps are facing a common challenge—which app development approach to choose? Should we build a native, mobile web, or hybrid app? More importantly, which is the most appropriate app type for you and your business?

Let’s find out what are native, mobile web, and hybrid apps, analyze the differences between them, as well as the advantages and disadvantages of each. After reading, you will be able to understand which approach will be the right one for your business idea.

Web Apps

A web app is a software program that uses web technologies like HTML5, CSS, JavaScript. It exists entirely within browsers such as Safari and Chrome and is essentially a mobile website. It is therefore not possible to download them from an app store.

Also, the web app is not connected with the device’s operating system and does not use its storage. Unlike an iOS or Android app, there is no software development kit (SDK) for a developer to work with. There are templates and frameworks for developing web apps like Angular, React, and Vue.js that you can use to get a quick start.

So, here are some pros of web application

• Developing a web app can be simple and quick. So, web apps are a cost-effective way to put your product in the hands of a lot of users.
• Web apps can be built for all platforms as long as they can run in an appropriate web browser.
• The user doesn’t have to manage updates manually. The newest version always loads when a user opens a web app.

And some cons:

• Searching for an app might be difficult because the user doesn’t find it in any app store.
• A browser is required to run a web app. It means that users have to take more steps to use a web app, whether that’s searching for the page or typing in a URL.
• Web apps can be slower and lack a lot of benefits. They only work online and they don’t have access to as many phone features like push notifications, contacts, etc.

However, as browsers and web apps become more advanced, PWAs (progressive web apps) offer a wider functionality like:

• Sending push messages
• Using some touch screen elements
• Using some of the device’s hardware

So, If your startup is on a budget and doesn’t require complex functionalities or access to operating system features, then building a web app can be the most suitable solution.

Native Apps

A native app is a software application that is built for specific platforms and is written in languages that the platform accepts. For example, Swift and Objective-C for native iOS apps, Java or Kotlin for native Android apps and C# for the most part for Windows Phone apps. Native apps are also built using the specific Integrated Development Environment (IDE) for the selected operating systems.

Advantages of the native application:

• Native apps offer the fastest, most reliable and most responsive experience to users.
• Good integration with the hardware of the device (e.g. camera, GPS, phone, touch screen)
• It comes with the push notification functionality.
• Great design and user experience possibilities. For example, the implementation of sophisticated UI/UX design and animation.
• These apps can be downloaded from app stores and installed on mobile devices, where they can be placed as a designated icon on the home screen.
• Native apps are also more suitable for working offline.

Disadvantages:

• Need more than one codebase. iOS apps will not run on Android and the opposite, so you will have to work with different codebases for every platform you choose to build in.
• Native apps are usually more expensive in developing, especially for companies that need apps on multiple OS platforms. For both platforms, it will require two separate developers or teams.
• A lot of time is spent on development. Also, it takes time to update the app for different platforms.
• Require constant updates to keep with the current OS versions.
• Expensive to keep your native app updated
• Both Apple App Store and Google Play get a 30% commission from every in-app payment.

To sum up, a bigger budget is required if you want to build your app for multiple platforms (i.e. iPhones and Android) and to keep your native app updated. Native apps are not the best option for simple applications.

By offering a great user experience, better performance and accessibility, native apps are able to offer users a more personalized product.

Hybrid apps

Hybrid apps combine the power of native and web apps to reap the benefits of both. Actually they are made, so they look and feel like native applications, but like web apps, are built with JavaScript, HTML, and CSS. Also, there is a shell that is downloadable and loads the code using a WebView.

Advantages of the hybrid apps:

• Hybrid apps don’t need a web browser like web apps.
• With a hybrid app, you only have one codebase to manage. So, instead of building two apps like with native apps, you’re building one app, and it works on both platforms.
• Hybrid app developers are often less expensive than native application developers. You will probably require half the number of developers two native apps would have required. Or, with the same number of developers, a hybrid app could be published in half the time.
• Hybrid is a great option for developers that create visually intense applications, for instance, games
• Hybrid apps let you retain the same ability to access device features as with native apps.
• Easy to work with, maintain and upgrade
• Because of their native infrastructure, hybrid apps can work offline.
• Hybrid apps are easier to scale to another platform. Once you’ve built for one platform, you can launch on another, like Windows Mobile.

The downside:

• Getting your hybrid app to run appropriately on each platform generally takes substantial work. Sometimes, the total cost might become comparable to that of fully native apps. It all depends on how close you want to get to the “native user experience” or how simple your app is.
• Hybrid apps can be slower than native apps.
• Need to customize to a large number of exciting browsers
• Inability to implement complex features (e.g. in-app analytics, charts, diagrams)
• These hybrid systems make bug fixing more difficult.
• Hybrid apps load in a WebView, so performance can be worse than native applications.

Say you have an idea for an app, and you don’t know if people will like it or not. If you understand that a web app doesn’t really allow you to test this app, the simple version of the hybrid app will be a good solution for you.

You give to people to download and use the app on their device, although you are short on resources. Usually, a startup project is decided to create a hybrid app that in the startup world, this is called an MVP, or minimum viable product.

Conclusion

There are many different directions in which you can take your app, all of which have their pros and cons. The choice between web, native, and hybrid development is dependent on a number of factors, including business needs, app requirements, developer skill, budget and timelines. What’s important is to spend enough time thinking and analyzing before you start building because the apps are expensive enough. So, you may only have one go at getting it right. Find out the information as much as you can about the different kinds of apps and the development stages.

Single-page applications (SPAs) have earned a strong position in demand long ago. Such apps have one page where most information remains unchanged, and only its parts are updated when a user interacts with them. 

Maybe, you don’t know precisely what they are. However, you probably use them daily. Most social media, Gmail, and other programs are single-page apps. They are quickly adopted thanks to their fast load times and user-friendliness. 

As for React, it is quite a favored instrument for SPA web development. Programmers can build SPA’s components with React to ensure their scalability, reuse them with few configurations, and refresh them instantly without manually reloading a page. Besides, React solutions are optimized for SEO while delivering high flexibility thanks to the Virtual DOM. 

What Are React Single Page Applications (SPAs)? 

SPAs consist of one HTML page. To access it, you make a one-time connection and then—load and modify data without refreshing an entire page. Only the blocks engaged in the interaction process are updated while other page elements remain unaffected.

A clear illustration of a one-page application is Google Docs. While reading a text, you scroll through sheets, but the top row with the menu and the left panel with captions stay put. Whatever changes you make, certain things stay untouched.

React is very common for SPAs because of its convenience—it speeds up the programming process significantly. Also, note that it is not a framework, it’s a library. The main thing in it is the components as they represent particular items on a page. Each one of them contains the entire set of data and methods needed for work.

React allows you to save the state of the elements. This makes them isolated and self-contained. This feature, in turn, provides developers with the ability to reuse the elements as needed. Specifically, the same element can be applied to another page or parts of the website as many times as you want. This way, a developer doesn't need to re-write a significant part of the code. Therefore, the absence of complex dependencies between elements simplifies troubleshooting.

Pros of React SPAs

Why do giant companies like Meta choose React SPAs? It becomes clear after looking at its main advantages: 

• Versatility. SPAs can be used from any device that has access to the Internet. 
• Accessibility. There are no problems with сompatibility, as well as memory limitations, capacity, or installation time.
• Enhanced usability. These apps are straightforward and enjoyable to operate.
• Better responsiveness. It can be ensured thanks to the high speed of data load.
• Ability to process large volumes of data. The device's memory does not limit the processing power of an app and the volumes of data it operates.
• Code reuse. React allows developers to reuse parts of code as often as needed in the same app. 

Let's look closely at the main merits of React SPA development. 

Enhanced User Experience (UX)

The primary advantage of a single web page application is immediate data update, resulting in a smooth sensation from using and improved responsiveness. Since SPA doesn't have to implement full-page reloads during use, the contents of a page refresh very quickly. The program only needs to connect to the server at the start, and then simply loads the necessary segments as a visitor needs them. In a single-page app, everything works quickly after the first load and doesn’t keep users waiting. 

Improved Developer Efficiency

Programmers who use React receive a lot of tools that simplify the assembling of the single-page application architecture. Moreover, React allows reusing code in software architecture to speed up the work. For instance, if you decide later to evolve a one-page app into a full-featured multi-page application, you’ll be able to reuse about 30% of the code instead of writing everything from scratch. Plus, it will help your startup save time and reduce costs.

Rich Interactivity

React is a freely accessible framework library that has a JSX (JavaScript XML) compiler with a focus on the users’ comfort as it provides prompt rendering. Thus, by utilizing React, you can create a UI-centric product which is common to SPAs that should have fluid interfaces, rapidly responding to the actions of users.

Cons of React SPAs

Surely, React SPA development has its drawbacks. Otherwise, every app would be written using this framework. 

The most important cons of these apps are:

• Lack of SEO optimization. The whole point is in the structure of such applications, which consist of just one page.
• Longer initial loading time. Since all user-side data of the application must be loaded during initialization, this can cause unpleasant delays for users.
• The client-side routing challenges. React may not be the best choice for creating applications with complex business logic and data routing.

So, let’s check each of them in more detail.

SEO Challenges

Single-page applications are quite problematic to index because not much text can be recognized by search robots. At the same time, you can’t put all the keywords on one page because it will look weird and lead to spam problems. What's more, the page will have only one URL.

You can eliminate this issue by resorting to server-side rendering. It's a way to render a one-page application on the server side. The app is still one-paged, but the main work is done on the server. The application receives a finished page with the required SEO elements from the server immediately after its initialization. However, due to the complex introduction of this technique, the cost of SPA development might be higher.

Complexity for Large Applications

Managing complex states and data traffic can be a problem in large SPAs. That’s why using React SPAs relates mostly to projects with rather limited functionality. At the same time, if applied skillfully, this library can still be useful when creating large projects—evidence of this is the digital giants that actively use it in the development of their services and applications. 

Initial Load Time

With React’s ability to load all content during initialization, people can wait a lot when opening a SPA first (in comparison to other websites). However, after initial loading, all other data will be pulled up automatically, and there will be no issues with delays in provisioning page content. 

Code splitting solves the problem of slow app loading by reducing the volume of data sent on the first session. Rather than uploading the entire code at once, users do it only with the parts that they need right now. This, in turn, facilitates the coding procedure.

React SPAs vs Traditional Multi-Page Applications (MPAs)

These kinds of apps are paralleled and characterized as competitors. However, multi-page applications (MPAs) are unlikely to lose the race because each alternative works for certain products and commercial pursuits. 

Let's look at the core strengths of each application type:

‍

‍

As you can see, each application type has its drawbacks and advantages. So, when choosing between them, come from the specifics and tasks of your company. 

What kind of companies should pay attention to one-page solutions?

• Products that run on a captivating platform with low data volumes. SPA is commonly applied to SaaS platforms, social media, and private communities. Some examples include Grammarly, Netflix, and Slack.
• Startups focused on usability. Single-page apps allow you to create an easy-to-use and intuitive interface that attracts the eye and encourages customers to explore the product. 
• Startups planning to go big. Later you can reuse some of the code of your single web page application. This helps reduce the time and expense involved in developing new, full-fledged digital solutions.
• Businesses for whom SEO is not a key metric. If your company doesn't require to be ranked high on Google from its first launch, to successfully implement your idea, SPA is right for you.
• Companies with their own APIs. You can build a one-page application based on a pre-built API.

Conclusion 

Today, we see that one-page applications gradually replace classic applications and begin to play an important role in the construction of large-scale web systems. Now, these apps are everywhere, and we use them every day without noticing it. Moreover, the big players in the app market already use SPAs.

What are the rewards of single-page applications? They are pretty speedy, don't keep visitors on hold, and generally improve their experience when dealing with the solution. in turn, overall consumer comfort has a direct impact on conversion rates and profits.

Still, SPAs are not ideal for all startups. But for those seeking a dynamic, high-speed, and lightweight digital solution, SPAs are an excellent choice.