You are not writing code for the compiler — it will eat any nonsense if the syntax is correct. You are writing for people, for that guy from the next department who will analyze your code in six months. For yourself, when you forget what you were thinking about when you wrote it. For the team leader, who does not have time to decipher your "features" disguised as tech debt.
Dirty code is about unclear variables, confusing modules and quick fixes. After this, you will lose time and, at best, get sideways glances from your colleagues. Unfortunately, unclear code is often written not only because of haste, but also because of inexperience and excessive enthusiasm of those who want to redo everything.
Have you seen those glowing eyes of juniors and zoomers when applying for a job? I think you can read in them: “your code is crooked, let me into production and I will do everything right..” and they start building their own “perfect” architectures.
So, code aesthetics is not a perfectionist’s fad, it is pragmatism, and the code should be written in such a way that the next developer will light a candle in your thoughts.
Let’s figure out how to turn a nightmare into candy — the details are inside.
What is "beautiful" code?
Good code is not just about "working", you don't want to see "plasticine" and sticks in production. It's about working clearly, maintainably and without pain. Structure, readability, logic - all this directly affects how effectively the team can develop the project. And this is immediately felt in the work.
Why does unreadable code slow down progress?
Hard-to-read code is difficult to correct and test possible solutions, so it takes more time to edit than regular code.
Why waste time searching for the connection between data and result, days - fixing bugs generated by non-obvious logic, if you can go to a coffee point with colleagues at this time.
Clean code is created for people who will read, edit and get paid for your past mistakes. So don't offend programmers and believe me, they will thank you when variables are named, for example, unverifiedOrder, and not tempList, when functions like validatePayment() do exactly what they promise, bugs become rare guests, and not constant neighbors.
Below I will give two abstract examples of Python code to show the difference:
Each task is taken out into a separate function: one function multiplies numbers, another converts strings, a third decides which handler to call depending on the data type, and a separate function is responsible for sorting the result. All have clear names and laconic docstrings. Such code is easy to read, it is easy to cover with tests, maintain and develop further, without fear of accidentally breaking something.
But you won’t be able to start writing like this right away, fortunately for your colleagues and to your disappointment. You will have to go through several iterations of “I messed something up here” and “I don’t even understand what I wrote anymore”. Until the first piece of code that someone on the team will say: “Wow, it’s done conveniently”.
However, if you change your thinking a little and master several skills, then your success will be much better, and you will come to writing beautiful code much faster. The good news is that everything comes with practice. First, let's define how to learn to write beautiful code, and what knowledge is needed for this, and then - with the skills that will need to be developed.
How is food related to code?
Of course, not directly, but spaghetti code is a figurative name used to describe chaotic, disorganized software code that is almost impossible to maintain. Imagine a ball of spaghetti - intertwined, branching threads where it is difficult to find the beginning and end. Spaghetti code resembles this ball, where fragments are intertwined with each other without obvious structure and logic.
Often, "spaghetti code" occurs when developers manage a project without a clear architecture, unified coding standards and a documentation system. Hasty edits, lack of a plan, constant changes lead to relaxed work, chaos, unreadability, making the project incomprehensible even to its author.
Here, all the logic of data processing, transformations and sorting is in one function. The code is not divided into separate blocks, which leads to confusion: conditions, cycles and sorting are mixed up, variable names do not reflect their purpose and there is no documentation. As a result, it is difficult to understand what each part of the code is responsible for (we are developing in IT, not in fortune telling), and accordingly, maintaining such code is extremely problematic.
Analyze
A study conducted at Atlassian presented graphs illustrating the results of code readability analysis for both human-written code and code generated by large language models.
I'll show you a few of them:
65% of respondents admitted that they spend more than 75% of their working time reading other people's (and their own) lines. It's like a writer rereading other people's novels instead of writing his own. Another 19% are immersed in reading code 50-75% of the time. And only 11% rarely spend time on it (<25%)
For 81% of respondents, this is a matter of project survival, while 7%, on the contrary, rate it as neutral or less important.
90% noted a significant reduction in the cost of code support in the future. About 87% indicated that well-structured code significantly simplifies the process of debugging and solving emerging problems. For 85%, an important factor is improving the understandability of the code for other team members. In turn, 82% believe that an emphasis on readability demonstrates the developer’s professionalism and emphasizes the aesthetics of the work. Team productivity and effective interaction within the group were important for 81% of respondents. Finally, 76% of respondents noted that such practices facilitate project scaling and code reuse.
79% of respondents indicated that the main barrier is a lack of time due to tight deadlines, forcing them to adhere to the principle "If it works, then it's good enough." For 65% of developers, functionality always remains a priority, and readability fades into the background. Another 58% admitted that they focus on other metrics, such as execution speed or system performance.
Problems with the code itself were mentioned by 70% of survey participants, who attribute difficulties in improving readability to outdated or confusing code. 61% face resistance from within the team or from customers, for whom attention to "beautiful code" seems excessive. Finally, 63% noted that the lack of clear standards or guidelines for writing code prevents them from working systematically - without these "recipes" they are forced to rely on intuition and experience.
86% — high-quality names of variables and functions, 84% — are put into comments, but not those that duplicate the code ("increment the counter by 1"), but those that explain why a strange algorithm was chosen here, 77% — obey code styles and standards (PEP8, Linter, etc.), 71% — look at the length of the code (names, functions, etc.), and another 58% — the structure (indents, brackets, etc.).
If we delve a little into history, then at the dawn of programming, code was closer to a set of machine instructions than to conscious design. Programs were written by small teams, and issues of readability and structure faded into the background. However, with the growth of system complexity and the transition to team development, code ceased to be a personal tool — it became the language of communication between developers.
Clean code has been a long-standing topic of discussion, with one of the most famous software development luminaries, Robert C. Martin (aka Uncle Bob), delving into the principles that define quality source code in his book Clean Code: A Handbook of Agile Software Craftsmanship, published in 2008. Granted, the book was published a long time ago, but it has inspired and continues to inspire generations of coders. In more recent news, in interviews and speeches, he emphasizes that a programmer should leave behind code that colleagues can understand and develop without pain. This is not a matter of personal style - it is a matter of respect for the team's work and the sustainability of the project in the long term. The concept of "clean code" has been systematically developed thanks to practices such as structured programming, modularity, as well as the principles of SOLID, DRY and KISS, which have become the cornerstones of the modern engineering approach to development. Below, we will consider their principles in more detail.
How does technical debt affect projects?
Every simplification or carelessness in the code accumulates like a snowball. First you forget about one thing, then another, and so on, and over time, fixing even small errors begins to require a disproportionate amount of resources. Clean code here becomes a risk management strategy: thoughtful architecture, meaningful abstractions, and strict modularity protect the project from turning into a "legacy monster." Code aesthetics are already part of the engineering culture; we have long understood that code is not just instructions for a machine, but a way to communicate with each other. Therefore, you want to write in such a way that the next developer does not suffer, but reads and understands the first time. Beautiful code is often compared to art: it not only pleases the eye, but also simplifies life - it is easier to read, edit, and supplement. Every engineer is a creator to some extent, but not every creator becomes a good engineer - this is the difference.
Broken Windows Theory in Development
You can draw a parallel between code quality and social psychology. Remember that story where a window broke in a house, and noticing that no one was going to fix it, people decided that it was okay to leave trash there - and so everything turned into a dump. I think there was even a cartoon about this - write in the comments.
So, by analogy, if there are sloppy sections in a project that no one fixes, this provokes a further decline in standards. One module with confusing logic can become a starting point for a general decline. Therefore, maintaining clean code is not an individual task, but a collective responsibility that forms a culture of quality in the team.
Architectural solutions and aesthetics
You can't build an elegant architecture at random, it's unlikely that you'll be "gifted" from birth, you need to be able to combine SOLID principles, design patterns and refactoring practices. This combination not only makes the proposal more reliable and faster, but also turns working with code into an aesthetic pleasure.
SOLID as a measure of architectural elegance
SOLID principles are a kind of "base" for creating a flexible and sustainable architecture. Of course, this is not a random set of letters, SOLID is an acronym that represents five basic principles of OOP and design, let's figure it out:
- Single Responsibility Principle (SRP): Each class or module should have only one reason to change. Simply put, one class, one task. For example, if a class is responsible for storing data, it should not also validate it or display it on the screen. Each area of responsibility is separate. This helps to avoid “garbage” and simplify support.
- Open/Closed Principle (OCP): Code should be open for extension, but closed for changes. That is, if we need to add new functionality, we do not get into the already working code, but add something new, for example, through inheritance or interfaces. This reduces the chance of breaking something, it is not worth breaking on purpose.
- Liskov Substitution Principle (LSP): Subtypes should be able to replace base classes without any problems — and everything should work correctly. If you pass a child class object to a function, it should behave like the parent class, and not break the logic. Violating this principle often causes bugs that are hard to catch.
- Interface Segregation Principle (ISP): It is better to have several narrow interfaces than one general but overloaded one, customization is always good. Classes should not depend on methods they do not use. This helps make the code modular, easily scalable and more readable.
- Dependency Inversion Principle (DIP): High-level components should not depend on details. And vice versa - both should rely on abstractions. That is, we calmly change the implementation without affecting the main logic.
The KISS principle calls for creating the simplest and most understandable solutions in programming. It originated in the 1960s in engineering practice and was adapted in software development. The essence of KISS is that simplicity makes it easier to understand, test and maintain code, reducing the likelihood of errors and simplifying the adaptation of new developers to the project. Therefore, we can say that KISS includes almost all other design principles, and, in my opinion, the KISS principle can be useful only for novice designers.
The DRY principle (Don't Repeat Yourself) is aimed at eliminating duplicate code and information in the system. It was formulated in the book "The Pragmatic Programmer" by Andy Hunt and Dave Thomas. According to DRY, each piece of knowledge should have a single, consistent and authoritative representation within the system. Or, more simply put, in the system we describe each piece of knowledge in one single and understandable way. Then, when you change one element, you do not have to edit everything around that is not related to it.
Design patterns
What existed much earlier than Clean Code was the concept of design patterns, and their maturity point was the book Design Patterns: Elements of Reusable Object-Oriented Software, 1994, by the famous Gang of Four. The authors collected the empirical experience of many projects and turned a set of disparate techniques into clear patterns for solving common problems in OOP.
Each pattern is not ready-made code, but an abstract scheme of interaction between classes and objects, helping to avoid typical architectural traps. No, this is not a “silver bullet” in the form of ready-made code, but rather a “meta-solution” - a conceptual model that requires adaptation to the specifics of the subject area.
Instead of specific implementations or frameworks, it operates with high-level ideas - delegation, composition, inversion of control - and requires adaptation to your project. This flexibility allows patterns to become the “building blocks” for scalable systems, reducing duplication and the risk of bottlenecks.
They fall into three main categories:
- Creational Patterns: Responsible for creating objects.
- Structural Patterns: Define ways to organize classes and objects to form larger structures.
- Behavioral patterns: Describe the ways in which objects interact.
Technical tools and practices for improving code
Here is a set of recommendations for optimizing your software production through clean code:
- Remind developers about clean code principles to ensure all your teams are on the same page. This is also a good time to consider training or a workshop on the topic.
- Implement a clean code policy by clearly defining the most important rules of precedence within your business. For example, by standardizing the names of variables or functions that you use regularly.
- Document the code produced by your teams so that it remains readable over time.
- Integrate a Linter, a utility that will allow you to maintain consistent code based on the rules you have set up and predefined. There are several utilities for different coding languages.
- Organize monitoring, ask developers to regularly check their code to ensure its cleanliness, and organize team meetings.
- Rework old codes. Just because they weren’t clean before doesn’t mean they shouldn’t follow your transition!
The Best Tools for “Clean Coding”
There is no magic button or tool that can “implant” clean code into your project or automatically tidy everything up. But there are a number of utilities that can significantly simplify this task and give the developer useful guidelines.
Linters are what give your code a style and while you write, they immediately highlight incorrect indents, uninformative names and other deviations from the rules. There are dozens of such solutions for each language: for example, Checkstyle for Java, Clinton for JavaScript, HTMLHint for HTML, Flake8 for Python.
Maven helps to structure Java projects, Prettier automates code alignment and formatting.
Jenkins automatically builds the project via Maven, connects SonarQube for deep code analysis and immediately runs JUnit tests. Such a continuous cycle does not allow bugs to fall into production and saves time on fixing “technical garbage”.
Let's sum it up and place our bets
Empirical research in software development confirms that projects with highly readable code reduce the time it takes to fix errors compared to systems where technical debt dominates. For example, a 2022 study showed that “bad” code contains 15 times more defects, and fixing them takes an average of 124% longer.
Thus, forming a culture of clean and “beautiful” code is a strategic choice that ensures sustainable growth and competitiveness of projects in an increasingly complex IT environment. Programmers and teams should perceive the aesthetics of code not as an additional burden, but as an investment in the future of their product and professional development.
It is also predicted that by 2028, 75% of software engineers in enterprises will use AI assistants for code, which is significantly higher than the ~10% at the beginning of 2023. But all this will only really work with basic discipline: if you honestly adhere to uniform naming rules, divide logic into neat modules and document every piece, the tools will start giving refactoring advice about 40% more accurately.
What is your attitude towards code aesthetics and the role of AI in development? Do you have your own pitfalls or life hacks — it would be interesting to discuss in the comments.
