[Complements]

Entropy in programming is a powerful, but often inconspicuous force, which determines the variability and unpredictability of software behavior. From simple bugs to complex grandflies, entropy is the reason that our programs do not always behave as we expect.

What is entropy in software?

Entropy in software is a measure of unexpected outcomes of algorithms. The user perceives 1stti outcomes as errors or bugs, but from the point of view of the machine, the algorithm performs exactly the instructions that the programmer laid down in it. Unexpected behavior arises due to a huge number of possible combinations of input data, system conditions and interactions.

Causes of entropy:

* Changing the state: When the object can change its internal data, the result of its work becomes dependent on the entire history of its use.

* The complexity of the algorithms: as the program grows, the number of possible ways to execute the code grows exponentially, which makes the prediction of all outcomes almost impossible.

* External factors: operating system, other programs, network delays – all this can affect the execution of your code, creating additional sources of variability.

Causes of entropy:

* Changing the state: When the object can change its internal data, the result of its work becomes dependent on the entire history of its use.

* The complexity of the algorithms: as the program grows, the number of possible ways to execute the code grows exponentially, which makes the prediction of all outcomes almost impossible.

* External factors: operating system, other programs, network delays – all this can affect the execution of your code, creating additional sources of variability.

Global variables as a source of entropy

In his work “Global Varia Bybles Consedered Harmful” (1973) W.A. Wulf and M. Shaw showed that global variables are one of the main sources of unpredictable behavior. They create implicit addictions and side effects that are difficult to track and control, which is a classic manifestation of entropy.

Laws of Leman and Entropy

The idea of growing complexity of software systems perfectly formulated Manny Leman in his laws of evolution of software. Two of them directly reflect the concept of entropy:

The computer program used will be modified. This statement suggests that the software is not static. It lives, develops and changes to meet new requirements and environment. Each new “round” of the life of the program is a potential source of entropy.

When the computer program is modified, its complexity increases, provided that no one prevents this. This law is a direct consequence of entropy. Without targeted complexity management efforts, each new modification introduces additional variability and unpredictability into the system. There are new dependencies, conditions and side effects that increase the likelihood of bugs and unobvious behavior.

Entropy in the world of AI and LLM: unpredictable code

In the field of artificial intelligence and large language models (LLM), entropy is especially acute, since here we are dealing with non -metnamic algorithms. Unlike traditional programs, where the same access always gives the same way out, LLM can give out different answers to the same request.

This creates a huge problem: the correctness of the algorithm can be confirmed only on a certain, limited set of input data using authors. But when working with unknown input data (requests from users), the behavior of the model becomes unpredictable.

Examples of entropy in LLM

Innordative vocabulary and racist statements: known cases when chat bots, such as Tay from Microsoft or GroK from XII, after training on data from the Internet, began to generate offensive or racist statements. This was the result of entropy: unknown input data in combination with a huge volume of training sample led to unpredictable and incorrect behavior.

Illegal appeals: such problems arise when a neural network begins to issue content that violates copyright or ethical norms.

AI Bota in games: the introduction of AI characters in games with the possibility of learning, for example, in Fortnite, led to the fact that AI Bot had to be turned off and added to tracking for the correctness of activity, to prevent illegal actions from the LLM bot.

Technical debt: accumulated interest on defects

Poorly written code and bypass solutions
Technical duty is a conscious or unconscious compromise, in which priority is given to rapid delivery to the detriment of long -term support and quality. Fast corrections and undocumented bypass solutions, often implemented in a short time, accumulate, forming a “minefield”. This makes the code base extremely sensitive even to minor changes, since it becomes difficult to distinguish intentional bypass solutions from actual erroneous logic, which leads to unexpected regression and an increase in the number of errors.

This demonstrates the direct, cumulative effect of technical duty on the spread of errors and the integrity of algorithms, where each current reduction adopted leads to more complex and frequent errors in the future.

Inadequate testing and its cumulative effect

When the software systems are not tested carefully, they are much more susceptible to errors and unexpected behavior. This inadequacy allows errors to accumulate over time, creating a system that is difficult to support and which is very susceptible to further errors. Neglecting testing from the very beginning not only increases technical debt, but also directly helps to increase the number of errors. The “theory of broken windows” in software entropy suggests that insignificant, ignored errors or design problems can accumulate over time and lead to more serious problems and reduce software quality.

This establishes a direct causal relationship: the lack of testing leads to accumulation of errors, which leads to an increase in entropy, which leads to more complex and frequent errors, directly affecting the correctness and reliability of algorithms.

Lack of documentation and information silos

Proper documentation is often ignored when developing software, which leads to fragmentation or loss of knowledge on how the system works and how to support it. This forces the developers to “back” the system for making changes, significantly increasing the likelihood of misunderstanding and incorrect modifications, which directly leads to errors. It also seriously complicates the adaptation of new developers, since critical information is not available or misleading.

Program entropy occurs due to “lack of knowledge” and “discrepancies between general assumptions and the actual behavior of the existing system.” This is a deeper organizational observation: entropy manifests itself not only at the code level, but also at the level of knowledge. These informal, implicit knowledge is fragile and are easily lost (for example, when leaving team members), which directly leads to errors when trying to modify, especially new members of the team, thereby jeopardizing the integrity of algorithmic logic, since its main assumptions cease to be clear.

inconsistent development methods and loss of ownership

The human factor is a significant, often underestimated, driving factor in software entropy. Various skills, coding and quality expectations among developers lead to inconsistencies and deviations in the source code. The lack of standardized processes for linting, code reviews, testing and documentation exacerbates this problem. In addition, an unclear or unstable code of the code, when several commands own part of the code or no one owns, leads to neglect and increase in decay, which leads to duplication of components that perform the same function in different ways, spreading errors.

This shows that entropy is not only a technical problem, but also a sociotechnical, deeply rooted in organizational dynamics and human behavior. “Collective inconsistency” arising due to inconsistent practices and fragmented possession directly leads to inconsistencies and defects, making the system unpredictable and difficult to control, which greatly affects the integrity of the algorithms.

Cascading malfunctions in interconnected systems

Modern software systems are often complex and very interconnected. In such systems, a high degree of complexity and closely related components increase the likelihood of cascading failures, when the refusal of one component causes a chain reaction of failures in others. This phenomenon exacerbates the influence of errors and improper behavior of algorithms, turning localized problems into systemic risks. The results of the algorithms in such systems become very vulnerable to failures that arise far from their direct path of execution, which leads to widespread incorrect results.

Architectural complexity, direct manifestation of entropy, can turn isolated algorithmic errors into large -scale system failures, making the general system unreliable, and its output data is unreliable. This emphasizes the need for architectural stability to contain the spread of entropy effects.

One of the latest examples is the well -known stopping of the airports in America and Europe due to the appearance of the blue death screen after updating antivirus software in 2024, the erroneous outcome of the antivirus algorithm and the operating system led to the air traffic in the world.

Practical examples

Example 1: Entropy in Unicode and byte restriction

Let’s look at a simple example with a text field, which is limited by 32 bytes.

Scenario with ASCII (low entropy)

If the field accepts only ASCII symbols, each symbol takes 1 bytes. Thus, exactly 32 characters are placed in the field. Any other symbol simply will not be accepted.

@startuml
Title Example with ASCII (low entropy)
Actor User
Participant “Textfield”

User -> TextField: introduces 32 symbols ASCII
TextField -> TextField: checks the length (32 bytes)
Note Right
Everything is fine.
End Note
TextField -> User: Acceps input
@enduml

Scenario with UTF-8 (high entropy):

Now our program of their 80s falls in 2025. When the field takes UTF-8, each symbol can occupy from 1 to 4 bytes. If the user introduces a line exceeding 32 bytes, the system can cut it incorrectly. For example, emoji occupies 4 bytes. If pruning occurs inside the symbol, then we get a “broken” symbol.

@startuml
Title Example with UTF-8 (high entropy)
Actor User
Participant “Textfield”

User -> TextField: introduces “Hi” (37 byte)
TextField -> TextField: Cuts the line up to 32 bytes
Note Right
Suddenly! Symbol
Cut by bytes.
End Note
TextField -> User: displays “Hi”
Note Left
Incorrect symbol.
End Note
@enduml

Here the entropy is manifested in the fact that the same pruning operation for different input data leads to unpredictable and incorrect results.

Example 2: Entropy in CSS and incompatibility of browsers

Even in seemingly stable technologies, like CSS, entropy can occur due to different interpretations of standards.

Imagine that the developer has applied user-Elect: None; To all elements to turn off the text output.

Browser 10 (old logic)

Browser 10 makes an exception for input fields. Thus, despite the flag, the user can enter data.

@startuml
Title browser 10
Actor User
Participant “Browser 10” As Browser10

User -> Browser10: Entering in INPUT
Browser10 -> Browser10: Checks CSS
Note Right
-user-Elect: None;
Ignored for Input
End Note
BROWSER10 -> User: Allows the Entering
@enduml

Browser 11 (New Logic)

The developers of the new browser decided to strictly follow the specifications, applying the rule to all elements without exception.

@startuml
Title browser 11
Actor User
Participant “Browser 11” As Browser11

User -> Browser11: Entering Input
Browser11 -> Browser11: checks CSS
Note Right
-user-Elect: None;
Applied to all elements, including Input
End Note
Browser11 -> User: Refuses to enter
Note Left
The user cannot do anything
type.
End Note
@enduml

This classic example of entropy – the same rule leads to different results depending on the “system” (version of the browser).

Example 3: Entropy due to an ambiguous TK

An ambiguous technical task (TK) is another powerful source of entropy. When two developers, Bob and Alice, understand the same requirement in different ways, this leads to incompatible implementations.

TK: “To implement a generator of Fibonacci numbers. For optimization, a list of generated numbers must be cocked inside the generator.”

Bob’s mental model (OOP with a variable condition)
Bob focused on the phrase “List … must be cocked.” He implemented a class that stores the same state (self.sequence) and increases it with every call.

    def __init__(self):
        self.sequence = [0, 1]

    def generate(self, n):
        if n <= len(self.sequence):
            return self.sequence

        while len(self.sequence) < n:
            next_num = self.sequence[-1] + self.sequence[-2]
            self.sequence.append(next_num)

        return self.sequence

Alice's mental model (functional approach)

Alice focused on the phrase "returns the sequence." She wrote a pure function that returns a new list each time, using cache only as internal optimization.

    sequence = [0, 1]
    if n <= 2:
        return sequence[:n]

    while len(sequence) < n:
        next_num = sequence[-1] + sequence[-2]
        sequence.append(next_num)

    return sequence

When Alice begins to use the Bob generator, she expects Generate (5) will always return 5 numbers. But if before this Bob called Generate (8) at the same object, Alice will receive 8 numbers.

Bottom line: Entropy here is a consequence of mental mental models. The changeable state in the implementation of Bob makes the system unpredictable for Alice, which awaits the behavior of pure function.

Entropy and multi -setness: the condition of the race and grandfather

In multi -flowing programming, entropy is manifested especially. Several flows are performed simultaneously, and the procedure for their implementation is unpredictable. This can lead to the Race Condition, when the result depends on which stream is the first to access the common resource. The extreme case is grandfather when two or more streams are waiting for each other, and the program freezes.

Example of the solution of Dedlok:

The problem of Dedlok arises when two or more stream block each other, waiting for the release of the resource. The solution is to establish a single, fixed procedure for seizing resources, for example, block them by increasing ID. This excludes a cyclic expectation that prevents the deadlock.

@startuml
Title Solution: Unified blocking procedure
Participant "Stream 1" as Thread1
Participant "Stream 2" as Thread2
Participant "AS" as Accounta
Participant "Account B" AS Accountb

Thread1 -> Accounta: blocks account a
Note Over Thread1
The rule follows:
Block ID
End Note
Thread2 -> Accounta: Waiting for the account A will be freed
Note Over Thread2
The rule follows:
Waiting for locking a
End Note
Thread1 -> Accountb: blocks account b
Thread1 -> Accounta: frees account a
Thread1 -> Accountb: releases score b
Note Over Thread1
The transaction is completed
End Note
Thread2 -> Accounta: blocks the account a
Thread2 -> Accountb: blocks account b
Note Over Thread2
The transaction ends
End Note
@enduml

This approach - ordered blocking (Lock Ordering) - is a fundamental strategy for preventing deadlles in parallel programming.

Great, let's analyze how the changeable state in the OOP approach increases entropy, using the example of drawing on canvas, and compare this with a pure function.

Problem: Changed condition and entropy

When the object has a changed state, its behavior becomes unpredictable. The result of calling the same method depends not only on its arguments, but also on the whole history of interaction with this object. This brings entropy into the system.

Consider the two approaches to the rectangle drawing on canvas: one in an oop-style with a variable condition, the other in a functional, with a pure function.

1. OOP approach: class with a variable state
Here we create a Cursor class, which stores its inner state, in this case, color. The DRAW method will draw a rectangle using this condition.

  constructor(initialColor) {
    // Внутреннее состояние объекта, которое может меняться
    this.color = initialColor;
  }

  // Метод для изменения состояния
  setColor(newColor) {
    this.color = newColor;
  }

  // Метод с побочным эффектом: он использует внутреннее состояние
  draw(ctx, rect) {
    ctx.fillStyle = this.color;
    ctx.fillRect(rect.x, rect.y, rect.width, rect.height);
  }
}

// Использование
const myCursor = new Cursor('red');
const rectA = { x: 10, y: 10, width: 50, height: 50 };
const rectB = { x: 70, y: 70, width: 50, height: 50 };

myCursor.draw(ctx, rectA); // Используется начальный цвет: red
myCursor.setColor('blue'); // Изменяем состояние курсора
myCursor.draw(ctx, rectB); // Используется новое состояние: blue

UML diagram of the OOP approach:

This diagram clearly shows that the call of the DRAW method gives different results, although its arguments may not change. This is due to a separate SetColor call, which has changed the internal state of the object. This is a classic manifestation of entropy in a changeable state.

title ООП-подход
actor "Программист" as Programmer
participant "Класс Cursor" as Cursor
participant "Canvas" as Canvas

Programmer -> Cursor: Создает new Cursor('red')
note left
  - Инициализирует состояние
    с цветом 'red'.
end note
Programmer -> Cursor: draw(ctx, rectA)
note right
  - Метод draw использует
    внутреннее состояние
    объекта (цвет).
end note
Cursor -> Canvas: Рисует 'red' прямоугольник
Programmer -> Cursor: setColor('blue')
note left
  - Изменяет внутреннее состояние!
  - Это побочный эффект.
end note
Programmer -> Cursor: draw(ctx, rectB)
note right
  - Тот же метод draw,
    но с другим результатом
    из-за измененного состояния.
end note
Cursor -> Canvas: Рисует 'blue' прямоугольник
@enduml

2. Functional approach: Pure function

Here we use a pure function. Its task is to simply draw a rectangle using all the necessary data that is transmitted to it. She has no condition, and her challenge will not affect anything outside her borders.

  // Функция принимает все необходимые данные как аргументы
  ctx.fillStyle = color;
  ctx.fillRect(rect.x, rect.y, rect.width, rect.height);
}

// Использование
const rectA = { x: 10, y: 10, width: 50, height: 50 };
const rectB = { x: 70, y: 70, width: 50, height: 50 };

drawRectangle(ctx, rectA, 'red'); // Рисуем первый прямоугольник
drawRectangle(ctx, rectB, 'blue'); // Рисуем второй прямоугольник

UML diagram of a functional approach:

This diagram shows that the Drawrectangle function always gets the outside color. Her behavior is completely dependent on the input parameters, which makes it clean and with a low level of entropy.

@startuml
Title Functional approach
Actor "Programmer" As Programmer
Participant "Function \ N Drawrectangle" as DRAWFUNC
Participant "Canvas" as canvas

Programmer -> DRAWFUNC: DRAWREWERCTANGLE (CTX, RECTA, 'RED')
Note Right
- Call with arguments:
- CTX
- RECTA (coordinates)
- 'Red' (color)
- The function has no condition.
End Note

DRAWFUNC -> Canvas: floods with the color 'Red'
Programmer -> DRAWFUNC: DRAWREWERCTANGLE (CTX, RECTB, 'Blue')
Note Right
- Call with new arguments:
- CTX
- RECTB (coordinates)
- 'Blue' (color)
End Note
DRAWFUNC -> Canvas: floods with the color 'Blue'
@enduml

In an example with a pure function, behavior is completely predictable, since the function has no condition. All information for work is transmitted through arguments, which makes it isolated and safe. In an OOP approach with a variable state to the behavior of the DRAW method, the whole history of interaction with the object can affect, which introduces entropy and makes the code less reliable.

Modular design and architecture: isolation, testability and re -use

The division of complex systems into smaller, independent, self -sufficient modules simplifies the design, development, testing and maintenance. Each module processes certain functionality and interacts through clearly defined interfaces, reducing interdependence and contributing to the separation of responsibility. This approach improves readability, simplifies maintenance, facilitates parallel development and simplifies testing and debugging by isolating problems. It is critical that this reduces the "radius of the defeat" of errors, holding back defects in separate modules and preventing cascading failures. Microservice architecture is a powerful realization of modality.

Modularity is not just a way of organizing code, but also a fundamental approach to containing defects and increasing stability. Limiting the impact of the error in one module, modality increases the overall stability of the system to entropy decay, guaranteeing that one point of refusal does not compromise the correctness of the entire application. This allows the teams to focus on smaller, more controlled parts of the system, which leads to more thorough testing and faster detecting and correcting errors.

Practices of pure code: kiss, drys and Solid principles for reliability

Kiss (Keep it Simple, Stupid):
This design philosophy stands for simplicity and clarity, actively avoiding unnecessary complexity. A simple code is inherently easier to read, understand and modify, which directly leads to a decrease in a tendency to errors and improve support. The complexity is clearly defined as a nutrient environment for errors.

KISS is not just an aesthetic preference, but a deliberate choice of design, which reduces the surface of the attack for errors and makes the code more resistant to future changes, thereby maintaining the correctness and predictability of algorithms. This is a proactive measure against entropy at a detailed level of code.

Dry (Donat Repeat Yourself):
The DRY principle is aimed at reducing the repetition of information and duplication of code, replacing it with abstractions or using data normalization. Its main position is that "each fragment of knowledge should have a single, unambiguous, authoritative representation in the system." This approach eliminates redundancy, which, in turn, reduces inconsistencies and prevents the spread of errors or their inconsistent correction in several copies of duplicated logic. It also simplifies the support and debugging of the code base.

Duplication of code leads to inconsistent changes, which, in turn, leads to errors. Dry prevents this, providing a single source of truth for logic and data, which directly contributes to the correctness of algorithms, guaranteeing that the general logic behaves uniformly and predictably throughout the system, preventing thin, difficult to enter errors.

Solid principles

This mnemonic acronym presents five fundamental design principles (unified responsibility, openness/closeness, substitution of Liskin, separation of interfaces, inversions of dependencies) that are crucial for creating object-oriented projects that are clear, flexible and supportive. Adhering to Solid, software entities become easier to support and adapt, which leads to a smaller number of errors and faster development cycles. They achieve this by simplifying service (SRP), ensuring the scalable adding functions without modification (OCP), ensuring behavioral consistency (LSP), minimizing coherence (ISP) and increasing flexibility due to abstraction (DIP).

Solid principles provide a holistic approach to structural integrity, which makes the system in essence more resistant to stunt effects of changes. Promoting modularity, separation and clear responsibilities, they prevent cascading errors and retain the correctness of algorithms even as the system is continuously evolution, acting as fundamental measures to combat entropy.

Entropy and Domain-Driven Design (DDD)

Domain-Driven Design (DDD) is not just a philosophy, but a full-fledged methodology that offers specific patterns for breaking the application into domains, which allows you to effectively control complexity and fight entropy. DDD helps to turn a chaotic system into a set of predictable, isolated components.

Patterns of Gang of Four design as a single conceptual apparatus

The book "Design Patterns: Elements of Reusable Object-Oriented Software" (1994), written by a "gang of four" (GOF), offered a set of proven solutions for typical problems. These patterns are excellent tools for combating entropy, as they create structured, predictable and controlled systems.

One of the key effects of patterns is the creation of a single conceptual apparatus. When the developer in one team talks about the "factory" or "loner", his colleagues immediately understand what kind of code is we talking about. This significantly reduces entropy in communication, because:

The ambiguity decreases: the patterns have clear names and descriptions, which excludes different interpretations, as in the example with Bob and Alice.

Oncuting accelerates: the new team members are poured faster into the project, since they do not need to guess the logic standing behind complex structures.

Refactoring is facilitated: if you need to change the part of the system built according to the pattern, the developer already knows how it is arranged and which parts can be safely modified.

Examples of GOF patterns and their influence on entropy:

Pattern "Strategy": allows you to encapsulate various algorithms in individual classes and make them interchangeable. This reduces entropy, as it allows you to change the behavior of the system without changing its main code.

Pattern "Command" (Command): Inkapsules the method of the method to the object. This allows you to postpone execution, put the commands in the queue or cancel them. Pattern reduces entropy, as it separates the sender of the team from its recipient, making them independent.

Observer Pattern (Observer): Determines the dependence of the "one-to-many", in which a change in the state of one object automatically notifies all dependent on it. This helps to control side effects, making them obvious and predictable, and not chaotic and hidden.

Pattern "Factory Method": defines the interface for creating objects, but allows subclasses to decide which class to institute. This reduces entropy, as it allows you to flexibly create objects without the need to know specific classes, reducing connectedness.

These patterns help programmers create more predictable, tested and controlled systems, thereby reducing entropy, which inevitably occurs in complex projects.

DDD key patterns for controlling entropy

Limited contexts: This pattern is the DDD foundation. It offers to divide a large system into small, autonomous parts. Each context has its own model, a dictionary of terms (Ubiquitous Language) and logic. This creates strict boundaries that prevent the spread of changes and side effects. Change in one limited context, for example, in the "context of orders", will not affect the "delivery context".

Aggregates (Aggregates): The unit is a cluster of related objects (for example, "order", "lines of the order"), which is considered as a whole. The unit has one root object (Aggregate Root), which is the only point of entry for all changes. This provides consistency and guarantees that the state of the unit always remains integral. By changing the unit only through its root object, we control how and when there is a change in the condition, which significantly reduces entropy.

Domain Services services: For operations that do not belong to any particular object of the subject area (for example, the transfer of money between accounts), DDD proposes to use domain services. They coordinate the actions between several units or objects, but do not keep the condition themselves. This makes the logic more transparent and predictable.

The events of the subject area (Domain Events): Instead of direct calling methods from different contexts, DDD offers to use events. When something important happens in one context, he "publishes" the event. Other contexts can subscribe to this event and respond to it. This creates a faint connectedness between the components, which makes the system a more scalable and resistant to changes.

DDD helps control entropy, creating clear boundaries, strict rules and isolated components. This turns a complex, confusing system into a set of independent, controlled parts, each of which has its own “law” and predictable behavior.

Complex and lively documentation

Maintaining detailed and relevant documentation on code changes, design solutions, architectural diagrams and user manuals is of paramount importance. This “live documentation” helps developers understand the intricacies of the system, track changes and correctly make future modifications or correct errors. It significantly reduces the time spent on the “re -opening” or the reverse design of the system, which are common sources of errors.

Program entropy occurs due to "lack of knowledge" and "discrepancies between general assumptions and the actual behavior of the existing system." The documentation acts not just as a guide, but as

The critical mechanism for preserving knowledge, which directly fights with the "entropy of knowledge." By making implicit knowledge explicitly and affordable, it reduces misunderstandings and the likelihood of making errors due to incorrect assumptions about the behavior of algorithms or system interactions, thereby protecting functional correctness.

strict testing and continuous quality assurance

Automated testing: modular, integration, system and regression testing
Automated testing is an indispensable tool for softening software entropy and preventing errors. It allows early detection of problems, guaranteeing that code changes do not violate the existing functionality, and provides quick, consistent feedback. Key types include modular tests (for isolated components), integration tests (for interactions between modules), system tests (for a full integrated system) and regression tests (to ensure that new changes do not lead to repeated appearance of old errors). Automated testing significantly reduces the human factor and increases reliability.

Automated testing is the main protection against the accumulation of hidden defects. It actively “shifts” the discovery of errors to the left in the development cycle, which means that problems are found when their correction is the cheapest and simplest, preventing their contribution to the effect of the snow coma of entropy. This directly affects the correctness of the algorithms, constantly checking the expected behavior at several levels of detail.

development through testing (TDD): shift to the left in the detection of errors

Development through testing (TDD) is a process of software development, which includes writing tests for code before writing the code itself. This iterative cycle "Red-Green-Refactoring" promotes quick feedback, allowing early detection of errors and significantly reducing the risk of complex problems at later stages of development. It was shown that TDD leads to a smaller number of errors and the optimal quality of the code, well coordinating the philosophy of Dry (Donat Repeat Yourself). Empirical studies of IBM and Microsoft show that TDD can reduce error density to release by impressive 40-90%. Test examples also serve as live documentation.

TDD acts as proactive quality control, built directly into the development process. Forcing the developers to determine the expected behavior before implementation, it minimizes the introduction of logical errors and guarantees that the code is created purposefully to comply with the requirements, directly improving the correctness and predictability of algorithms from the very beginning.

Continuous integration and delivery (CI/CD): Early feedback and stable releases
CI/CD practices are fundamental for modern software development, helping to identify errors in the early stages, accelerate development and ensure uninterrupted deployment process. Frequent integration of small code packages into the central repository allows early detection of errors and continuous improvement of code quality through automated assemblies and tests. This process provides quick feedback, allowing the developers to quickly and effectively eliminate problems, and also significantly increases the stability of the code, preventing the accumulation of unverified or unstable code.

CI/CD conveyors function as a continuous mechanism for reducing entropy. By automating integration and testing, they prevent the accumulation of integration problems, provide a constantly unfolding condition and provide immediate visibility of regression. This systematic and automated approach directly counteracts the disorder made by continuous changes, maintaining the stability of algorithms and preventing the spread of errors throughout the system.

Systematic management of technical debt

INCREATIONAL Refactoring: Strategic Code Improvement

Refactoring is the process of restructuring the existing code to improve its internal structure without changing its external behavior. This is a direct means of combating software rotting and reducing complexity. Although refactoring is usually considered a way to reduce the number of errors, it is important to admit that some refractoring can unintentively contribute new errors, which requires strict testing. However, studies generally confirm that the refractured code is less subject to errors than unscareuded. Increting refactoring, in which the debt management is integrated into the current development process, and is not postponed, is crucial to prevent the exponential accumulation of technical debt.

Refactoring is a deliberate action to reduce entropy, proactive code restructuring to make it more resistant to changes, thereby reducing the likelihood of future errors and improving the clarity of algorithms. It turns reactive extinguishing fires into proactive management of structural health.

Backlogs of technical debt: Prioritization and distribution of resources

The maintenance of a current Bablog of technical debt is a critical practice for systematic management and elimination of technical debt. This backlog serves as a comprehensive register of identified elements of technical duty and areas requiring improvement, guaranteeing that these problems will not be overlooked. It allows project managers to prioritize debt elements based on their seriousness of influence and potential risks. The integration of the Bablog during the project ensures that refactoring, error correction and code cleaning are regular parts of the project’s daily management, reducing the long -term repayment costs.

The baclog of technical debt turns an abstract, growing problem into a controlled, effective set of tasks. This systematic approach allows organizations to take reasonable compromises between the development of new functions and investments in quality, preventing inconspicuous debt accumulation, which can lead to critical errors or degradation of algorithm productivity. It provides visibility and control over key entropy power.

Static and dynamic code analysis: proactive identification of problems

Static analysis

This technique includes an analysis of the source code without its implementation to identify problems such as errors, code smells, safety vulnerability and impaired coding standards. It serves as the “first line of protection”, identifying problems in the early stages of the development cycle, improving the overall quality of the code and reducing technical debt by identifying problematic templates before they appear as errors during execution.

Static analysis acts as an automated "Code Quality Police". Identifying potential problems (including those that affect algorithmic logic) before performing, it prevents their manifestation in the form of errors or architectural disadvantages. This is a scalable method of ensuring coding standards and identifying common errors that contribute to software entropy.

Dynamic analysis

This method evaluates software behavior during execution, providing valuable information about problems that manifest only during execution. It excellently discovers errors during execution, such as memory leaks, the condition of the race and the exclusion of the zero pointer, as well as narrow places in performance and vulnerability of safety.

Dynamic analysis is critical for identifying behavioral disadvantages during execution, which cannot be detected by static analysis. The combination of static and dynamic analysis ensures a comprehensive idea of the structure and behavior of the code, allowing the teams to identify defects before they develop into serious problems.

Monitoring Production and Office of Incidents

APM (Application Performance Monitoring):
APM tools are designed to monitor and optimize applications performance. They help to identify and diagnose complex problems of performance, as well as detect the root causes of errors, thereby reducing loss of income from downtime and degradation. APM systems monitor various metrics, such as response time, use of resources and error frequency, providing real-time information, which allows you to proactively solve problems before they affect users.

APM tools are critical of proactive solutions to problems and maintaining service levels. They provide deep visibility in the production environment, allowing the teams to quickly identify and eliminate problems that can affect the correct algorithms or cause errors, thereby minimizing downtime and improving user experience.

Observability (logs, metrics, tracer):

The observability refers to the ability to analyze and measure the internal states of systems based on their output data and interactions between assets. Three main pillars of observability are metrics (quantitative data on productivity and use of resources), logs (detailed chronological records of events) and tracing (tracking the flow of requests through system components). Together they help to identify and solve problems, providing a comprehensive understanding of the behavior of the system. The observability goes beyond traditional monitoring, helping to understand "unknown unknown" and improving the time of trouble -free application of applications.

The observability allows the teams to flexibly investigate what is happening and quickly determine the root cause of the problems that they may not have foreseen. This provides a deeper, flexible and proactive understanding of the behavior of the system, allowing the teams to quickly identify and eliminate unforeseen problems and maintain high accessibility of applications.

Analysis of the root cause (RCA)

The analysis of the root causes (RCA) is a structured process based on data that reveals the fundamental causes of problems in systems or processes, allowing organizations to implement effective, long -term solutions, and not just eliminate symptoms. It includes the definition of the problem, collection and analysis of the relevant data (for example, metrics, logs, temporary scales), determination of causal and related factors using tools such as “5 why” and Ishikawa diagrams, as well as the development and implementation of corrective actions. RCA is crucial to prevent re -occurrence of problems and training on incidents.

RCA is crucial for the long -term prevention of problems and training on incidents. Systematically identifying and eliminating the main causes, and not only symptoms, organizations can prevent re -occurrence of errors and algorithms failures, thereby reducing the overall system of the system and increasing its reliability.

Flexible methodologies and team practices

Error management in Agile:

In the AGILE environment, error management is critically important, and it is recommended to allocate time in sprints to correct them. Errors should be recorded in a single product of the product and associated with the corresponding history to facilitate the analysis of root causes and improve the code in subsequent sprints. Teams should strive to correct errors as soon as possible, preferably in the current sprint in order to prevent their accumulation. The collection of error statistics (the number of resolved, the number of registered, hours spent on correction) helps to get an idea of code quality and improve processes.

This emphasizes the importance of immediate corrections, analysis of root causes and continuous improvement. Flexible methodologies provide a framework for proactive control of errors, preventing their contribution to the entropy of the system and maintaining the correctness of algorithms by constant verification and adaptation.

Devops practices

DevOPS practices help reduce software defects and improve quality through several key approaches. They include the development of a culture of cooperation and unmistakable communication, the adoption of continuous integration and delivery (CI/CD), the configuration of automated testing, focusing attention on observability and metrics, avoiding handmade work, including safety at the early stages of the development cycle and training on incidents. These practices reduce the number of errors, improve quality and contribute to constant improvement.

Devops contributes to continuous improvement and reduction of entropy through automation, quick feedback and a culture of general responsibility. Integrating the processes of development and operation, Devops creates an environment in which problems are detected and eliminated quickly, preventing their accumulation and degradation of systems, which directly supports the integrity of the algorithms.

Conclusion

Program entropy is an inevitable force that constantly strives for degradation of software systems, especially in the context of the correctness of algorithms and errors. This is not just physical aging, but a dynamic interaction between the code, its environment and human factors that constantly make a mess. The main driving forces of this decay include growing complexity, the accumulation of technical debt, inadequate documentation, constantly changing external environments and inconsistent development methods. These factors directly lead to incorrect results of the work of algorithms, the loss of predictability and an increase in the number of errors that can cascadely spread through interconnected systems.

The fight against software entropy requires a multifaceted, continuous and proactive approach. It is not enough just to correct errors as they occur; It is necessary to systematically eliminate the main reasons that generate them. The adoption of the principles of modular design, clean code (Kiss, Dry, Solid) and complex documentation is fundamental for creating stable systems, which are essentially less susceptible to entropy. Strict automated testing, development through testing (TDD) and continuous integration/delivery (CI/CD) act as critical mechanisms of early detection and prevention of defects, constantly checking and stabilizing the code base.

In addition, the systematic management of technical debt through incidental refactoring and bafflogists of technical debt, as well as the use of static and dynamic code analysis tools, allows organizations to actively identify and eliminate problem areas before they lead to critical failures. Finally, reliable production monitoring with the help of APM tools and observability platforms, in combination with a disciplined analysis of the root causes and flexible team practices, ensures rapid response to emerging problems and creates a continuous improvement cycle.

Ultimately, ensuring the integrity of algorithms and minimizing errors in conditions of software entropy - this is not a one -time effort, but a constant obligation to maintain order in a dynamic and constantly changing environment. Applying these strategies, organizations can significantly increase the reliability, predictability and durability of their software systems, guaranteeing that algorithms will function as planned, even as they evolution.

Currently, all popular LLM service providers use Fine-Tune using JSONL files, which describe the inputs and outputs of the model, with small variations, for example for Gemini, Openai, the format is slightly different.

After downloading a specially formed JSONL file, the process of specialization of the LLM model on the specified dataset begins, for all current well -known LLM providers this service is paid.

For Fine-Tune on a local machine using Ollama, I recommend relying on a detailed video from the YouTube channel Tech with TIM-Easiest Way to fine-tune a llm and us it with alloma:
https://www.youtube.com/watch?v=pTaSDVz0gok

An example of a JUPYTER laptop with the preparation of JSONL Dataset from exports of all Telegram messages and launching the local Fine-Tune process is available here:
https://github.com/demensdeum/llm-train-example

React Native has established itself as a powerful tool for cross-platform development of mobile and web applications. It allows you to create native applications for Android and iOS, as well as web applications using a single code base on JavaScript/Typescript.

Fundamentals of architecture and development

React National architecture is based on native bindings from JavaScript/Typescript. This means that the basic business logic and an application in the application are written on JavaScript or Typescript. When access to specific native functionality (for example, the device or GPS camera) is required, these native bindings are used, which allow you to call the code written on SWIFT/Objective-C for iOS or Java/Kotlin for Android.

It is important to note that the resulting platforms may vary in functionality. For example, a certain functionality can be available only for Android and iOS, but not for Web, or vice versa, depending on the native capabilities of the platform.

Configuration and updates
The configuration of native bindings is carried out through the Plugins key. For stable and safe development, it is critical to use the latest versions of React Native components and always turn to current documentation. This helps to avoid compatibility problems and use all the advantages of the latest updates.

Features of development and optimization

React Native can generate resulting projects for specific platforms (for example, Android and iOS folders). This allows the developers, if necessary, patch the files of resulting projects manually for fine optimization or specific settings, which is especially useful for complex applications that require an individual approach to performance.

For typical and simple applications, it is often enough to use EXPO Bandle with built -in native bindings. However, if the application has complex functionality or requires deep customization, it is recommended to use REACT NATIVE custom assemblies.

Eaturability of development and updates

One of the key advantages of React Native is Hot ReLoad support for Typescript/JavaScript code during development. This significantly accelerates the development process, since the code changes are instantly displayed in the application, allowing the developer to see the result in real time.

React Native also supports “Silent Update) bypassing the process of Google Play and Apple App Store, but this is only applicable to Typescript/JavaScript code. This allows you to quickly release errors or small functionality updates without the need to go through a full cycle of publication through applications stores.

It is important to understand that TS/JS Code is bandaged on a specific version of native dependencies using fingerprinting, which ensures the coordination between JavaScript/Typescript part and native part of the application.

Use of LLM in development

Although codhegeneration with LLM (Large Language Models) is possible, its suitability is not always high due to potentially outdated datasets on which the models were trained. This means that the generated code may not correspond to the latest versions of React Native or the best practices.

React Native continues to develop, offering developers a flexible and effective way to create cross -platform applications. It combines the speed of development with the possibility of access to native functions, making it an attractive choice for many projects.