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Deep Learning (DL) is at the core of numerous technologies that millions of people use every day worldwide, including self-driving car algorithms, Netflix and Amazon Prime Video recommendations, and voice assistants like Siri or Google Assistant. Just a few decades ago this technology seemed like science fiction, and yet today it has become an integral part of our lives. Deep learning uses multilayer neural networks to reveal hidden patterns, such as recognizing facial features in a picture or deciphering text, and is inspired by the structure of the human brain. In this article, we’ll talk about the fundamentals of deep learning and its principles, learn how it works, and describe its application areas. What is Deep Learning? Deep learning is a rapidly evolving field of artificial intelligence that enables computers not just to follow commands, but to learn and solve complex problems independently while processing large volumes of data. Think of a program that can recognize a face in a picture, understand spoken language, translate between languages, or even forecast stock prices. That’s deep learning in action. The word “deep” refers to the many layers within neural networks. The more layers, the deeper and more capable the network becomes. In traditional programming, developers explicitly write out algorithms. However, deep learning allows the system to determine how to complete a task on its own. For example, to teach a system to recognize cats in images, you don’t need to define features like whiskers, paws, or tails; just show it thousands of images, and it will learn the key traits on its own. Deep learning isn’t just a technology; it’s an approach that helps machines reach a level of perception closer to human cognition. Deep Learning vs. Machine Learning Think of machine learning as the broader category. It includes any technique where computers learn from data. Classical ML includes algorithms like linear regression, decision trees, random forests, and support vector machines, which work well with structured data. But these approaches struggle with unstructured data like images, audio, or text. In those cases, human intervention is often required to manually engineer features (like edges in an image). Deep learning, on the other hand, eliminates the need for manual feature engineering. You provide raw data — pixels, sound waves, etc. — and the model figures out what's important. Its core principle is hierarchical data representation. For instance, in face recognition: The first layer might detect edges The second identifies shapes like eyes or mouths The third combines them into facial features Deeper layers recognize complete faces with unique traits How Does Deep Learning Work? Deep learning is based on artificial neural networks. These networks process input data through many layers to produce outputs such as a predicted object or a translated phrase. Here’s how the learning process works: Input: Data is fed into the neural network. Feature Detection: Each layer detects increasingly complex features. Prediction: The network produces an output (e.g., label or value). Error Correction: The result is compared with the correct answer. If there’s an error, the network adjusts using backpropagation. Iteration: This process repeats thousands or millions of times until the network becomes accurate. Key components include: Activation functions (e.g., ReLU, sigmoid): decide which signals to pass. Optimizers (e.g., stochastic gradient descent): help minimize error. Computational resources: GPUs and TPUs accelerate training dramatically. Deep Neural Networks: Structure and Principles A Deep Neural Network (DNN) typically includes: Input Layer: Receives raw data (e.g., image pixels or audio features). Hidden Layers: The core of the network where data is transformed. Output Layer: Produces final predictions (e.g., “cat” 80%, “dog” 15%). Each layer is made up of mathematical units (neurons) that multiply, sum, and activate values to pass to the next layer. Training a Neural Network: Key Steps Training a neural network is a step-by-step process similar to human learning: Data Collection: Large, diverse, and high-quality datasets are essential. Preprocessing: Clean the data, normalize it, and split into train/validation/test sets. Choosing Architecture: Decide on the network type (CNN, RNN, Transformer) and layer count. Initialization: Start with random weights for neurons. Training: Iterate through the data (epochs), updating weights via backpropagation. Evaluation: Test accuracy on unseen data to avoid overfitting. Tuning: Adjust hyperparameters (learning rate, batch size) to improve performance. This process may take hours or even days, especially for large models, but the results are often worth it. Where Is Deep Learning Used? Deep learning has touched nearly every aspect of life enabling machines to see, hear, understand, and predict. In Computer Vision A major domain for DL, using Convolutional Neural Networks (CNNs) for: Face recognition: in smartphones, security, and social media. Self-driving cars: identifying signs, pedestrians, and other vehicles. Image processing: automatic filtering, retouching, and OCR. CNNs excel at detecting spatial patterns, from small details to full scenes. In Natural Language Processing (NLP) DL is revolutionizing language tasks using RNNs and transformers for: Translation: tools like Google Translate or DeepL. Chatbots: responsive and conversational systems. Sentiment analysis: identifying emotion in text (e.g., reviews). Models like BERT and GPT-3 elevated NLP by enabling contextual understanding. In Medicine, Finance, and More DL goes beyond software, impacting real-world applications: Healthcare: diagnosing diseases from X-rays/MRIs, predicting illness, aiding drug development. Finance: fraud detection, market trend analysis, algorithmic trading. Entertainment: training AI in games, generating music, creating virtual characters. Industry: quality control, equipment failure prediction, logistics optimization. Science: genome analysis, climate modeling, physics simulations. Deep learning is a universal tool that adapts to diverse challenges. Popular Tools and Libraries for Deep Learning Powerful and user-friendly tools are essential for working with deep learning. The top favorites are TensorFlow and PyTorch. TensorFlow is a library developed by Google. It is known for its scalability and support for industrial-grade projects and is well-suited for complex systems and model deployment. PyTorch is ideal for prototyping and is very popular among researchers due to its flexibility and ease of experimentation. Both libraries support GPU computing, have active communities, and offer extensive documentation. In addition, there are other software tools available for deep learning: Keras: A high-level API for TensorFlow, simple and intuitive, ideal for beginners. MXNet: A lightweight and efficient library used by Amazon for scalable solutions. Caffe: Specialized in computer vision, widely used in academic research. Theano: One of the earliest deep learning libraries, now less popular but historically significant. You should choose your tool based on your goals: Research & experimentation: PyTorch Production & deployment: TensorFlow Beginner-friendly: Keras Specialized tasks: Caffe, MXNet Where to Start with Deep Learning Core Knowledge Needed To begin with deep learning, you should understand: Math: Linear algebra (vectors, matrices), statistics (distributions, probability), and optimization (gradients). Programming: Python is essential, along with libraries like NumPy, Pandas, and Matplotlib. Basic ML Concepts: Regression, classification, and evaluation metrics. Courses, Books, and Resources Great learning materials include: Courses: Deep Learning Specialization by Andrew Ng (Coursera) CS231n: CNNs for Visual Recognition (Stanford) Fast.ai for hands-on learning Books: Deep Learning by Goodfellow, Bengio, Courville Neural Networks and Deep Learning by Michael Nielsen (free online) Resources: YouTube: 3Blue1Brown, Sentdex Blogs: Towards Data Science, Distill.pub Where to Find Code and Projects Explore and experiment with real-world examples on: GitHub: Thousands of open-source DL projects. Kaggle: Competitions, datasets, and shared notebooks. Google Colab: Free cloud-based notebooks with GPU support. Papers with Code: Research papers with source code. The Future of Deep Learning Deep learning continues to evolve rapidly. In the near future, we can expect: Efficient models: Less data and computation (e.g., TinyML for edge devices). Quantum computing: Faster training with quantum processors. Ethics & regulation: Addressing model bias and ensuring transparency. However, challenges remain: High energy consumption (leading to financial costs) Technology accessibility for small companies Security concerns (e.g., model vulnerability to attacks) Even so, deep learning has already transformed the world and continues to unlock new frontiers. Conclusion Deep learning is more than just a technology. It’s a revolution in IT. Built on multilayered neural networks, it allows machines to find patterns in data autonomously, bringing them closer to human-like perception. From theoretical models to the core of modern tech, DL has come a long way in a short time. Despite challenges like energy demands and ethical concerns, its future remains bright, helping make machines smarter and the world more technologically advanced.

Anyone who decides to get into programming faces an important question: which language should they learn first? For a beginner unfamiliar with software development's nuances, trying to answer this alone can easily lead to mistakes. Choosing hastily comes with a risk, as you might fall into the sunk cost trap. You could end up picking a language that doesn't align with your desired direction, whether it's mobile development, game dev, or systems programming. Relearning another language later may render much of your time and effort wasted. So, it is of course much better to make the right decision early. And for this, you need at least a general understanding of how each language works: its specifics, features, areas of application. In this article, we’ll present both complex and beginner-friendly programming languages, to help  beginners make an informed choice. Why It’s Important to Choose the Right Programming Language There are several reasons why it's crucial to study the features of each language at the very beginning and pick the most suitable one: Task suitability. Every language has its own strengths. One might be better suited for a specific set of tasks than another. Your chosen development field heavily affects your tech stack, especially in today's world, where specialization is becoming more prominent. The days when operating systems, programs, and games were all written in one language are gone. Now, there's a tool for everything. Community support. Any popular programming language has an active community, extensive documentation, and plenty of frameworks and libraries. However, more complex languages (like C++) can be harder to work with regarding libraries and documentation. You should take this into account. Career prospects. Learning a high-demand language opens more job opportunities and makes it easier to find projects that align with your interests and specialization. Scalability and performance. Some tasks require special features from a language, like efficient memory management or support for parallel computing. Sometimes, these factors are critical. So, clearly understanding which language to start learning can help avoid many future issues, and at best, guide you into an exciting direction and a successful developer career. Python: The Best Starting Point Python is a high-level, interpreted programming language with dynamic typing. Dynamic typing means the variable type is determined at runtime and can change. This adds flexibility but increases the chance of errors. Static typing means a variable's type is set at compile time and can't change. Type errors are caught earlier. For example, in a dynamically typed language, you could first assign the number 7 to a variable and later assign a string like "Hello, World" to that same variable. In a statically typed language, this would cause a compile-time error. Interpreted languages execute code directly without first converting it to machine code. Compiled languages, on the other hand, convert high-level code into machine instructions, making them generally faster. Python was initially created by Dutch programmer Guido van Rossum in 1991. Today, it is maintained by the global Python Steering Council and the nonprofit Python Software Foundation. Simple Syntax Python’s key feature is its use of indentation and colons instead of curly braces to define code blocks: if True: print("One block of code") else: print("Another block of code") This simplifies the language and makes the code more visually readable, especially in Object-Oriented Programming: class Butler: def __init__(self, name): self.name = name def hello(self): print(f"The butler of this mansion welcomes you — {self.name}!") butler = Butler("Alfred") butler.hello() # Output: The butler of this mansion welcomes you — Alfred Python aims to be both clear and functional, using as few syntax elements as possible (like braces or semicolons). Versatile Applications Thanks to its clean syntax and line-by-line execution, Python can be used in a wide variety of fields: Web Development. Building the backend of web apps, handling user requests (RESTful APIs), and generating dynamic web pages. Machine Learning. Processing and analyzing large datasets, building ML models, and creating neural networks. It’s also widely used in scientific computing across physics, biology, and engineering. Automation. As a scripting language, Python is used to automate routine tasks, manage servers, and streamline DevOps workflows. Despite its power and use in large-scale infrastructure and serious applications, Python remains the most beginner-friendly programming language. Large Community and Documentation Python is used globally across industries and research, resulting in a massive community of developers, engineers, and scientists. Regular conferences like PyCon, EuroPython, and PyData foster idea-sharing and collaboration. Online platforms like StackOverflow and Reddit host extensive discussions on Python coding nuances. The official documentation provides detailed language syntax, standard libraries, and step-by-step guides with examples, covering even the most basic topics. JavaScript: The Language of Web Development JavaScript is a high-level, interpreted programming language with dynamic typing. It was developed in 1995 by Brendan Eich at Netscape. Its name's similarity to Java was a marketing decision rather than a technical one. Java was extremely popular at the time, and the name helped boost interest in the new language. The Core of Frontend Development Modern browsers come with a built-in JavaScript engine to run scripts that manipulate the DOM (Document Object Model) to dynamically change a web page’s content: <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>DOM Manipulation</title> </head> <body> <div id="container"> <p>This is the original text.</p> </div> <button id="changeBtn">Change Content</button> <script> const container = document.getElementById('container'); const button = document.getElementById('changeBtn'); button.addEventListener('click', function() { const firstParagraph = container.querySelector('p'); firstParagraph.textContent = "Text changed!"; const newParagraph = document.createElement('p'); newParagraph.textContent = "A new paragraph was added to the DOM."; container.appendChild(newParagraph); }); </script> </body> </html> Thanks to JavaScript, developers can create interactive UIs for modern web apps. Scripts run directly in any browser, so no extra software is needed. This makes JavaScript one of the most accessible programming languages for beginners. A Rich Ecosystem Web development with JavaScript is a whole industry on its own. There are countless libraries and frameworks for managing web app states, such as React and Vue. But JavaScript isn’t limited to the client side. With Node.js, JavaScript can also run on servers. That's why many JavaScript applications and libraries are isomorphic, meaning they work both on the front and backend. Because of this flexibility, JavaScript is a solid first programming language, helping you become a Full Stack developer (handling both frontend and backend). Java: Stability and Demand Java is a high-level, object-oriented programming language with static typing. It was developed in 1995 by Sun Microsystems (later acquired by Oracle), led by James Gosling. Java is a compiled language. Its source code is compiled into intermediate bytecode, which is executed by the Java Virtual Machine (JVM). Since JVMs are implemented for different operating systems, Java code is cross-platform and can run on any OS without recompilation. That’s why Java’s slogan is: "Write once, run anywhere." Android Development Android is an OS with many components written in different languages. While its kernel is in C and C++, app development libraries and APIs are Java-based. This has made Java almost synonymous with mobile development, including both apps and games. For example, the popular game Minecraft was written in Java and, almost immediately after its PC release in 2011, was added to the Google Play Market as a mobile version for Android. High Performance Unlike interpreted programming languages, Java uses JIT (Just-in-Time) compilation. When an application is run, the bytecode is dynamically compiled into machine code so that frequently used code segments are optimized on the fly. On one hand, Java delivers higher performance than interpreted languages, such as JavaScript or Python. On the other hand, the indirect execution of bytecode is slower than direct execution of machine instructions in compiled languages like C or C++. Java is quite a fast language, especially considering that it runs through a virtual machine to provide strong cross-platform compatibility. Popularity in the Corporate Environment Cross-platform capabilities, application portability, predictable behavior, stability, and security are key reasons why many companies prefer Java. And of course, its rich ecosystem—libraries, frameworks, and tools—all contribute to simplifying and accelerating enterprise application development, maintenance, and updating. In contrast to Python, Java uses a strict C-like syntax: public class Greeter { private String name; // Constructor that takes a name for greeting public Greeter(String name) { this.name = name; } // Method that prints the greeting to the console public void greet() { System.out.println("Hello, " + name + "!"); } public static void main(String[] args) { // If a command-line argument is provided, use it as the name. Otherwise, default to "World" String name = (args.length > 0) ? args[0] : "World"; Greeter greeter = new Greeter(name); greeter.greet(); } } C#: A Windows Developer's Alternative C# is a high-level, object-oriented programming language with static typing. However, dynamic typing is also possible using the dynamic keyword. The C# programming language first appeared in 2002. It was created by Microsoft under the leadership of engineer Anders Hejlsberg.  Like Java, C# code is not compiled directly into machine instructions but into an intermediate representation called CIL (Common Intermediate Language) or simply IL. During program execution, the CIL code is converted via JIT compilation into native machine code optimized for the target platform. Used in .NET Development and Unity .NET is a development platform created by Microsoft for building portable applications. It can be used to develop websites, mobile apps, games, neural networks, and cloud services. The .NET ecosystem includes: Programming languages (C#, F#, VB.NET) Libraries and frameworks (for databases, servers, graphics) Runtimes C# is the main language in the .NET ecosystem. To some extent, Java and .NET can be seen as similar ecosystems. In Java, apps run on the JVM (Java Virtual Machine), while in .NET, they run on the CLR (Common Language Runtime). In both cases, code is compiled to bytecode, which is then executed on a virtual machine. Moreover, Java and C# have syntactic similarities, as both are C-style languages. Naturally, game development has also embraced C#. For instance, the popular Unity game engine uses C# as the primary scripting language for creating gameplay mechanics and scenarios. Strong Microsoft Support Microsoft plays a key role in the development of C#. This support includes the language itself, tooling, libraries, and infrastructure. C# integrates well with other Microsoft products and is tailored to the Microsoft ecosystem, although it remains cross-platform. For example, the Visual Studio IDE is best optimized for the C# compiler. A simple C# console application looks like this: using System; class Program { static void Main() { Console.Write("May I have your name?: "); string name = Console.ReadLine(); Console.WriteLine($"Welcome, {name}!"); } } C and C++: For Systems Programming C and C++ are compiled programming languages that are closely related. C++ is an extended, object-oriented version of the procedural C language. C was created at Bell Labs by Dennis Ritchie in 1972, while C++ was introduced by Bjarne Stroustrup in 1983. High Performance Unlike Python, JavaScript, and Java, C and C++ do not require an interpreter or a virtual machine. Their code is compiled directly into processor instructions. In other words, these languages are as close to the hardware as possible, allowing low-level control of system resources. That’s also why these languages are considered complex—manual control and lack of automation demand high programmer skill. Memory Management C and C++ give full control over computing resources. They do not include a garbage collector that automatically frees unused memory. This reduces overhead but increases the risk of memory leaks. Due to their performance and control, C and C++ are preferred for high-load computing, like OS kernels (Linux, Windows, macOS, Android), game engines (Unreal Engine), and financial systems. In short, C and C++ remain the go-to languages when speed and efficiency are critical. Foundation for Modern Languages Originally developed for Unix-like OS development, C became the ancestor of many modern languages. Its syntax is the foundation of many popular languages: C++, Java, C#, JavaScript, Go, Swift. Example of simple C++ code using classes: #include <iostream> #include <string> class Car { private: std::string brand; public: Car(std::string carBrand) { brand = carBrand; } void showInfo() { std::cout << "Car brand: " << brand << std::endl; } }; int main() { Car myCar("Toyota"); myCar.showInfo(); return 0; } Swift: For iOS Development Swift is a modern high-level, statically typed language that is compiled into machine instructions. Apple’s Official Language Before Swift, Apple’s main language was Objective-C, dating back to the 1980s. Despite its power, it had outdated principles and lacked support for modern syntax and safe memory handling. In 2014, Apple introduced Swift, a modern, safe, and convenient language aimed at improving code writing, safety, performance, and memory management. In short, Swift was created as Apple’s new official language for iOS, macOS, watchOS, and tvOS development. Simplified Syntax Compared to Objective-C Objective-C: NSString *name = @"John"; NSInteger age = 25; NSArray *fruits = @[@"Apple", @"Banana", @"Orange"]; - (void)greet:(NSString *)name { NSLog(@"Hello, %@", name); } [self greet:@"Alice"]; Swift: let name = "John" var age = 25 let fruits = ["Apple", "Banana", "Orange"] func greet(name: String) { print("Hello, \(name)") } greet(name: "Alice") As a result, Swift has cleaner and more understandable syntax, which means faster development. A Great Choice for Mobile Development Swift is optimized for Apple’s custom chips. It’s the main language for developing native iOS applications and games. Apple actively supports and develops the Swift ecosystem, and it is fully integrated into Xcode, Apple’s official IDE. Go: A Modern Language from Google Go, or Golang, is a high-level, statically typed programming language designed with concurrency in mind. It was developed in 2007 by Google engineers Robert Griesemer, Ken Thompson, and Rob Pike. Google created Go to address speed, concurrency, and development convenience issues found in other languages, like: C++: Powerful but complex Java: Bytecode execution is slower than native Python: Simple but slow due to interpretation As a company focused on cloud services, Google made Go with server-side development in mind. Simplicity and Minimalism Go has automatic garbage collection, a simple syntax, and convenient abstractions, but it's not a classical OOP language. There are no classes, no this keyword, no method/operator overloading. Instead, Go uses structs with methods attached to them: package main import "fmt" type Person struct { Name string Age int } func (p Person) Greet() { fmt.Println("Hi, my name is", p.Name) } func main() { person := Person{Name: "Anna", Age: 35} person.Greet() } Go minimizes complexity and accelerates development by unifying syntax: No classes/inheritance: use structs and interfaces instead. No method/operator overloading: one name per method. No exceptions: use explicit error checks Exceptions are errors thrown by an application during execution that can be caught and handled by user-written code without terminating the program. At first glance, such simplification may seem to limit the programmer’s capabilities. However, in reality, a strict definition of application logic provides greater flexibility in possible implementations and solutions. This is likely why Go, along with Python and JavaScript, is considered one of the best programming languages for beginners. High Performance Go code is compiled to machine instructions. The lack of heavy features like overloading and exceptions makes Go programs high-performing. The garbage collector is optimized for minimal delays. Instead of OS level threads, Go uses goroutines—lightweight threads that use only a few kilobytes of memory and can be spawned in large numbers. Great for Server-Side Development Compiled code, concurrency support, and minimal syntax make Go ideal for backend development. Built-in packages for web servers, networking, databases, and encoding (like net/http, database/sql, encoding/json) allow out-of-the-box server app development. Which Programming Language Should You Choose First? So. When choosing a programming language, several important factors should be taken into account: Development field. In what area will you be working? Web applications, systems programming, game development? And if it’s game development, what kind? Mobile or desktop? Or maybe even console games? Future tasks. The tasks you’ll face will depend on the area of development. But the type of tasks can also vary. For example, web development includes both frontend and backend. In game development, there’s engine development and gameplay mechanics creation. Entry threshold. Depending on personal aptitudes and existing skills, learning a specific programming language will be different for every developer. For instance, effective use of C and C++ requires deeper computer science knowledge: understanding memory management, algorithm complexity, and mathematical computations. The showcased languages can be compared across several key parameters: Language Syntax Complexity Execution Model Type System Year Released Official Site Primary Field Additional Knowledge Python Low Interpretation Dynamic 1991 python.org Data Analysis Machine Learning, Big Data JavaScript Low Interpretation Dynamic 1995 - Web Development Layout, Network Protocols Java Medium Compilation Static 1995 java.com Mobile Development Android, DevOps C# Medium Compilation Static 2002 microsoft.com Desktop Development .NET C/C++ High Compilation Static 1972 / 1985 isocpp.org Systems Programming Mathematics Swift Medium Compilation Static 2014 swift.com Mobile Development macOS, iOS Go Medium Compilation Static 2012 go.dev Servers, Microservices RESTful APIs, Containerization Learning the syntax of a specific language is best done with the help of books.  You can clarify various details through tutorial articles and videos. And when problems arise during learning or work tasks, you can ask questions on platforms like StackOverflow. Ultimately, the choice always comes down to personal preference. In the long run, it’s better to pursue something you are actually interested in rather than just something profitable. Otherwise, you'll just burn out.

You can write code. You can edit existing code. You can even rewrite it from scratch. There’s a lot you can do with code. But what’s the point if the code lives in its own echo chamber? If the same person writes, views, and edits it, many critical errors can drift from one version to another unnoticed without external evaluation. Code locked within the confines of a single text editor is highly likely to stagnate, accumulating inefficient constructs and architectural decisions, even if written by an experienced developer. This is why every developer should understand what code review is, how it’s done, and what tools are needed. Presenting your code properly to others, gathering feedback, and making changes wisely is important. Only this way can code remain “fresh” and efficient, and applications based on it — secure and high-performing. Code review is the process of examining code by one or more developers to identify errors, improve quality, and increase readability. Types of Code Review 1. Formal Review A formal review is a strict code-checking process with clearly defined stages. It’s used in critical projects where errors can have serious consequences — for example, in finance or healthcare applications. The analysis covers not just the code but also the architecture, performance, and security. Reviewers often include not just developers but also testers and analysts. For example, a company developing a banking app might follow these steps: Development: A developer completes a new authentication module and submits a pull request via GitHub. Analysis: A review group (2 senior developers + 1 security specialist) is notified and checks the code for logic, readability, and security (e.g., resistance to SQL injection and XSS attacks). Discussion: Reviewers meet the developer over Zoom and give feedback. Documentation: All notes are posted in GitHub comments and tracked in Jira. For instance, some RESTful requests may be flagged as vulnerable with a recommendation to use parameterized queries. Fixes: The developer updates the code and the pull request; the cycle repeats until approval. Approval: Once reviewers are satisfied, the code is merged into the main branch. 2. Informal Review Informal code review is less strict and more flexible, usually involving: Quick code discussions in chat or meetings Showing code to a colleague in person Asking an expert a technical question This kind of review happens often in day-to-day work and is characterized by spontaneity, lack of documentation, informal reviewer choice, and shallow checks. In simpler terms, it’s more like seeking advice than a formal third-party audit. It's a form of knowledge sharing. Types include: Over-the-Shoulder Review: One developer shows their code to another in real time (via screen share, chat message, or simply turning the monitor). Ad-hoc Review: A developer sends code to a colleague asking them to check it when convenient, e.g., I wrote this handler, but there’s an error. Can you take a look? Unstructured Team Review: Code is discussed at a team meeting, casually and collaboratively, often with knowledge sharing. Feedback is given as recommendations, not mandates. Developers can ignore or reject suggestions. Although informal reviews are less reliable than formal ones, they’re quicker and easier, and often complement formal reviews. Examples of integration: Preliminary Checks: Before a pull request, a dev shows code to a colleague to discuss and fix issues. Informal Discussion During Formal Review: Reviewers may chat to resolve issues more efficiently. Quick Fixes: Developers make changes right after oral feedback instead of long comment exchanges. 3. Pair Programming Pair programming is when two developers work together on one machine: one writes code, and the other reviews it in real-time. It’s literally simultaneous coding and reviewing, which helps catch bugs early. Roles: Driver: Writes code, focused on syntax and implementation. Navigator: Reviews logic, looks for bugs, suggests improvements, and thinks ahead. Roles can be switched regularly to keep both engaged. Variants: Strong Style: Navigator makes decisions, and the driver just types. It works well if one of the developers is more experienced. Loose Pairing: Both share decision-making, swapping roles as needed. Though rare, pair programming has advantages: Instant Feedback: Bugs are fixed immediately. In-depth Review: The second dev is deeply involved in writing the code. On-the-job Learning: Juniors learn directly from experienced peers. It’s more of a collaborative development method than a strict review. 4. Automated Review Automated code review uses tools that analyze code for errors, style, and vulnerabilities without human intervention. These tools are triggered automatically (e.g., after compilation, commit, or pull request). They analyze, run tests (e.g., unit tests), and generate reports. Some tools can even auto-merge code if it passes checks. Automated code review is part of DevOps and is common in CI/CD pipelines before deploying to production. Types: Static Analysis: Checks code without executing it — syntax errors, bad patterns, etc. Dynamic Analysis: Runs code to detect memory leaks, threading issues, and runtime errors. However, for now, tools can't catch business logic or architectural issues. As AI evolves, tools will likely become better at "understanding" code. When is Code Review Needed? Ideally, you should conduct code reviews both in small and large-scale projects. The only exceptions might be personal side-projects (pet projects), although even these can benefit from outside input. Automated testing has become standard, from JavaScript websites to C++ libraries. Still, code review can be skipped for: Trivial changes (e.g., formatting, UI text updates) Peripheral code (e.g., throwaway scripts, config files) Auto-generated code — unless manually modified In short, review the code only if it plays a critical or central role in the app and a human wrote it. Main Stages of Conducting Code Review Regardless of whether a review is formal, informal, or automated, there are several common stages. Preparation for Review Whether the written code is a new component for a production application or a modification of an existing method in a personal project, the developer is usually motivated to have it reviewed, either by fellow developers or by using automated testing tools. Accordingly, the developer has goals for the review and a rough plan for how it should be conducted, at least in broad terms. It’s important to understand who will participate in the review and whether they have the necessary competencies and authority. In the case of automated testing, it’s crucial to choose the right tools. Otherwise, the goals of the review may not be achieved, and critical bugs might remain in the code. Time constraints also matter: when all reviewers and testing tools will be ready to analyze the code, and how long it will take. It’s best to coordinate this in advance. Before starting the actual review, it can also be helpful to self-review—go over the code yourself and try to spot any flaws. There might be problems that can be fixed immediately. Once the developer is ready for the review, they notify the reviewers via chat, pull request, or just verbally. Code Analysis and Error Detection Reviewers study the code over a period of time. During this process, they prepare feedback in various formats: suggested fixes in an IDE, chat comments, verbal feedback, or testing reports. The format of the feedback depends on the tools used by the development team, which vary from project to project. Discussion of Edits and Recommendations Reviewers and the developer conduct a detailed discussion of the reviewed codebase. The goal is to improve the code while maintaining a productive dialogue. For instance, the developer might justify certain controversial decisions and avoid making some changes. Reviewers might also suggest non-obvious improvements that the developer hadn't considered. Documentation and Task Preparation All identified issues should be clearly documented and marked. Based on this, a list of tasks for corrections is prepared. Kanban boards or task managers are often used for this, e.g., Jira, Trello, and GitHub Issues. Again, the documentation format depends on the tools used by the team. Even a solo developer working on a personal project might write tasks down in a physical notebook—or, of course, in a digital one. Though keeping tasks in your head is also possible, it’s not recommended. Nowadays, explicit tracking is better than implicit assumptions. Relying on memory and intuition can lead to mistakes. Applying Fixes and Final Approval Once the list of corrections is compiled, the developer can begin making changes. They often also leave responses to comments. Bringing code to an acceptable state may take several review rounds. The process is repeated until both reviewers and the developer are satisfied. It’s crucial to ensure the code is fully functional and meets the team’s quality standards. After that, the final version of the code is merged into the main branch—assuming a version control system is being used. Tools for Code Review In most cases, code review is done using software tools. Broadly speaking, they fall into several categories: Version control systems: Most cloud platforms using version control systems (typically Git) offer built-in review tools for viewing, editing, and commenting on code snippets. Collaboration tools: Development teams often use not just messengers but also task managers or Kanban boards. These help with discussing code, assigning tasks, and sharing knowledge. Automated analyzers: Each programming language has tools for static code analysis to catch syntax issues, enforce style rules, and identify potential vulnerabilities. Automated tests: Once statically checked, the code is run through automated tests, usually via language-specific unit testing libraries. This article only covers the most basic tools that have become standard regardless of domain or programming language. GitHub / GitLab / Bitbucket GitHub, GitLab, and Bitbucket are cloud-based platforms for collaborative code hosting based on Git. Each offers tools for convenient code review. On GitHub and Bitbucket, this is called a Pull Request, while on GitLab it’s a Merge Request. Process: The developer creates a Pull/Merge Request documenting code changes, reviewer comments, and commit history. Reviewers leave inline comments and general feedback. After discussion, reviewers either approve the changes or request revisions. Each platform also provides CI/CD tools for running automated tests: GitHub Actions GitLab CI/CD Bitbucket Pipelines These platforms are considered the main tools for code reviews. The choice depends on team preferences. The toolas are generally similar but differ in details. Crucible Atlassian Crucible is a specialized tool dedicated solely to code review. It supports various version control systems: Git, SVN, Mercurial, Perforce. Crucible suits teams needing a more formalized review process, with detailed reports and customizable settings. It integrates tightly with Jira for project management. Unlike GitHub/GitLab/Bitbucket, Crucible is a self-hosted solution. It runs on company servers or private clouds. Pros and cons: Platform Deployment Managed by Maintenance Complexity GitHub / GitLab / Bitbucket Cloud Developer Low Atlassian Crucible On-premise End user/admin High Crucible demands more setup but allows organizations to enforce internal security and data policies. Other Tools Each programming language has its own specialized tools for runtime and static code analysis: C/C++: Valgrind for memory debugging Java: JProfiler, YourKit for profiling; Checkstyle, PMD for syntax checking Python: PyInstrument for performance; Pylint, Flake8 for quality analysis These tools often integrate into CI/CD pipelines run by systems like GitHub Actions, GitLab CI, CircleCI, Jenkins. Thus, formal code review tools are best used within a unified CI/CD pipeline to automatically test and build code into a final product. Best Practices and Tips for Code Review 1. Make atomic changes Smaller changes are easier and faster to review. It’s better to submit multiple focused reviews than one large, unfocused one. This aligns with the “Single Responsibility Principle” in SOLID. Each review should target a specific function so reviewers can focus deeply on one area. 2. Automate everything you can Automation reduces human error. Static analyzers, linters, and unit tests catch issues faster and more reliably. Automation also lowers developers’ cognitive load and allows them to focus on more complex coding tasks. 3. Review code, not the developer Code reviews are about the code, not the person writing it. Criticism should target the work, not the author. Maintain professionalism and use constructive language. A good review motivates and strengthens teamwork. A bad one causes stress and conflict. 4. Focus on architecture and logic Beautiful code can still have flawed logic. Poor architecture makes maintenance and scaling difficult. Pay attention to structure—an elegant algorithm means little in a badly designed system. 5. Use checklists for code reviews Checklists help guide your review and ensure consistency. A basic checklist might include: Is the code readable? Is it maintainable? Is there duplication? Is it covered by tests? Does it align with architectural principles? You can create custom code review checklists for specific projects or teams. 6. Discuss complex changes in person Sometimes it’s better to talk in person (or via call) than exchange messages—especially when dealing with broad architectural concerns. For specific code lines, written comments might be more effective due to the ability to reference exact snippets. 7. Code should be self-explanatory Good code speaks for itself. The simpler it is, the fewer bugs it tends to have. When preparing code for review, remember that other developers will read it. The clarity of the code affects the quality of the review. Put yourself in the reviewers’ shoes and ensure your decisions are easy to understand. Conclusion Code review is a set of practices to ensure code quality through analysis and subsequent revisions. It starts with syntax and architecture checks and ends with performance and security testing. Reviews can be manual, automated, or both. Typically, new code undergoes automated tests first, then manual review—or the reverse. If everything is in order, the code goes into production. If not, changes are requested, code is updated, and the process is repeated until the desired quality is achieved.