Unit Test Best Practices: Top 10 Tips with Examples

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Learn the best practices for unit testing. Improve your testing efficiency and ensure software reliability in 2025.

Sep 26, 2025

0 mins read

Shivam Agarwal

Unit tests verify small, isolated code sections (like functions, methods, or classes) to ensure they perform as designed. In contrast, integration tests examine how multiple components work together. Best practices for unit tests help standardize them to be effective, readable, reliable, fast, and maintainable. These practices shift testing from reactive bug-finding to proactive quality building. The core principle is isolation: unit tests must be independent of external factors like databases, networks, or file systems, ensuring a test fails only because of a flaw in that specific unit's code.

Top 10 Unit Test Best Practices

Write descriptive test names – make the purpose clear at a glance.
Keep tests independent – avoid hidden dependencies between tests.
Follow the Arrange–Act–Assert (AAA) pattern – structure tests consistently.
Test one thing at a time – focus on a single behavior per test.
Use meaningful assertions – check outcomes that reflect real behavior.
Avoid fragile tests – don’t rely on implementation details.
Maintain test readability – clean, self-explanatory test code.
Mock and stub wisely – isolate units without overusing fakes.
Automate tests in CI/CD – ensure reliability and early detection of issues.
Review and refactor tests regularly – keep your test suite maintainable.

Why Does Unit Testing Matter?

1) Early Bug Detection and Exponential Cost Savings

The most widely cited benefit of unit testing is its role in early defect detection, which has a profound economic impact on the software development lifecycle. The cost to fix a bug is not static; it grows exponentially the later it is discovered.

According to industry analysis, fixing a bug that has reached production is 30 to 100 times more expensive than fixing it during the initial coding phase. Even a bug found during a later testing phase, such as integration or system testing, is already 15 to 50 times more costly to resolve than one caught immediately by a unit test. These costs are not just financial; they include developer time spent on debugging, context switching, and rework, all of which detract from new feature development.

Given that software testing and quality assurance can account for 15-25% of a total project budget—and up to 40-50% for mission-critical systems in finance or healthcare—the efficiency gains from early detection are substantial. Research from 2025 indicates that organizations with mature, early-stage testing practices, anchored by unit testing, report a significant reduction in post-release defects. This directly mitigates external failure costs, which include expensive customer support cycles, potential product recalls, and intangible but severe damage to brand reputation.

2) Enabling Safer Refactoring and Architectural Evolution

A comprehensive and reliable unit test suite functions as a critical "safety net" for the development team. This safety net gives developers the confidence to refactor and improve the codebase's internal structure without the paralyzing fear of inadvertently breaking existing functionality.

In today's agile environments, software is never truly "done." Codebases must constantly evolve to accommodate new features, changing business requirements, and technological advancements. Without a robust test suite, code becomes rigid and brittle. Developers become hesitant to make necessary changes, leading to the accumulation of technical debt—a state where the cost of future development is mortgaged by poor design choices made in the past. Unit tests are the primary tool for preventing this architectural decay, enabling the continuous improvement that is the hallmark of a healthy, long-lasting software project.

3) Tests as Living, Executable Documentation

Well-written unit tests are arguably the most effective and reliable form of documentation for a codebase. They provide clear, executable examples of how a unit of code is intended to be used. By reading the tests associated with a method or class, a developer can quickly understand its purpose, its expected inputs, and its behavior under a variety of scenarios, including critical edge cases.

Unlike traditional, static documentation (such as comments or external documents), which can quickly become outdated and misleading, a unit test suite is "living documentation." It is continuously validated with every test run. If the production code changes in a way that invalidates the documentation provided by the tests, the tests will fail, forcing the developer to reconcile the code and its documented behavior. This ensures that the documentation remains accurate and trustworthy throughout the life of the project.

The true return on investment (ROI) of unit testing, therefore, extends far beyond the immediate bugs it catches. The conversation within engineering teams must evolve from "How much time does writing tests take?" to "How much time, money, and future opportunity does it preserve?" The data on bug-fixing costs demonstrates a clear financial case , but the strategic value lies in enabling agility. A strong test suite unlocks the ability to refactor and adapt , which is the engine of agile development. The absence of tests leads to technical debt and a fear of change, which slows down all future work. Consequently, framing the investment in testing as an investment in future development velocity shifts the practice from a tactical chore to a strategic imperative for any forward-looking engineering organization.

What Are the Core Principles of Unit Testing?

A high-quality unit test adheres to a set of core principles that ensure it is effective, efficient, and trustworthy. These characteristics are often summarized by the acronym FIRST: Fast, Isolated, Repeatable, Self-Checking, and Timely. Adherence to these principles is not merely a matter of following rules; it creates a virtuous cycle that maximizes developer productivity and confidence.

1) Isolation: The Cornerstone of Reliability

Test isolation is a foundational principle of unit testing. A unit test must be executed separately from other tests and, crucially, from external dependencies such as databases, file systems, or network services.

TTo achieve this separation, developers often use dependency injection, a technique where an object’s dependencies are provided to it from an external source rather than created internally. This approach makes it simple to replace real dependencies with test doubles—objects that stand in for the real ones in a test environment. Common types of test doubles include:

Mocks: Objects that simulate the behavior of real dependencies and can be programmed with expectations about how they should be called.
Stubs: Objects that provide pre-determined answers to calls made during the test.

By using mocks and stubs, a unit test ensures that its success or failure depends solely on the correctness of the unit under test. This prevents a test from failing due to external factors, like a network outage or a slow database query. Isolation also prevents cross-test interference, a frustrating scenario where the outcome of one test affects another, leading to a cascade of failures that are difficult to debug.

2) Small and Focused: One Behavior, One Test

Each unit test case should be designed to verify one single, specific behavior or logical concept. This practice of "one test, one behavior" is fundamental to creating a test suite that is easy to understand and maintain.

When a test is small and focused, its purpose is immediately clear. If it fails, the developer knows exactly which piece of functionality is broken, dramatically reducing debugging time. A common anti-pattern to avoid is including multiple "Act" steps within a single test method. If a second behavior needs to be tested, a second, separate test should be written.

3) Fast Execution and Repeatability

Unit tests must execute extremely quickly. Mature projects can have thousands of unit tests, and the entire suite must be runnable in minutes, not hours. Individual tests should complete in milliseconds. This speed is essential because it encourages developers to run the tests frequently—ideally, after every small code change. This provides a rapid feedback loop, allowing bugs to be caught and fixed moments after they are introduced.

Closely related to speed is repeatability. A unit test must be repeatable, producing the same result every time it is run, provided the production code has not changed. To achieve this, tests should rely on fixed test data (mocks or stubs) instead of unpredictable external systems. When randomness is a factor, it should be controlled using a seeded random number generator to ensure a predictable outcome. Consistency in test outcomes is what builds a team's trust in their test suite as a reliable indicator of code health.

4) Determinism: Guaranteed Consistency

A deterministic test is one whose outcome is predictable and does not depend on variable external factors. Such factors include the current date or time, random number generators, or the specific environment in which the test is run.

Non-deterministic tests, often called "flaky" tests, are a significant threat to the value of a test suite. A test that passes sometimes and fails at other times without any changes to the code erodes developer confidence. Teams quickly learn to ignore flaky tests, which is a dangerous habit, as a real failure might be dismissed as just more flakiness. For example, any test that relies on

DateTime.Now is inherently non-deterministic and will produce different results on different days. To test time-dependent logic, the concept of time must be abstracted (e.g., via an interface) and controlled within the test.

5) Clarity: Descriptive Naming Conventions

The name of a unit test should be descriptive enough to communicate its purpose without requiring a developer to read the test's code. Clear naming conventions are a form of documentation and a powerful tool for debugging. When a test fails, its name should immediately inform the team which scenario or behavior is broken.

A highly effective and widely adopted naming convention is the MethodName_Scenario_ExpectedBehavior pattern.

MethodName: The name of the method being tested.
Scenario: The specific condition or state being tested (e.g., "NegativeNumbers," "NullInput").
ExpectedBehavior: The expected outcome for that scenario (e.g., "ThrowsArgumentException," "ReturnsZero").

An example of this convention in practice would be a test named Sum_NegativeAndPositiveNumbers_ReturnsCorrectSum. This name is self-documenting and provides precise information in a test failure report.

These core principles are not an arbitrary checklist but rather an interconnected system. A violation of one principle often cascades, leading to the violation of others. For instance, a test that is not properly isolated and relies on a real database cannot be fast. If it is not fast, developers will not run it frequently, which defeats the purpose of rapid feedback. That same dependency on an external system makes the test non-repeatable and non-deterministic, as the state of the database can change between runs. Similarly, if a test is not

focused on a single behavior, its name cannot be truly clear, and a failure becomes a puzzle to diagnose. Adhering to these principles creates a virtuous cycle: isolation enables speed and determinism, which builds trust and encourages frequent execution. This, in turn, provides the rapid, reliable feedback that is the ultimate goal of unit testing.

How Should You Structure Tests for Maximum Clarity and Maintainability?

Beyond the core principles that define a good test, the structure of the test code itself plays a crucial role in its readability and long-term maintainability. Adopting consistent structural patterns allows developers to understand tests quickly and reduces the cognitive load required to work with the test suite.

The Arrange-Act-Assert (AAA) Pattern in Action

The Arrange-Act-Assert (AAA) pattern is a simple yet powerful convention for structuring the body of a test method. It divides the test into three logical, distinct sections, enhancing clarity and making the test's intent immediately obvious.

Arrange: In this first section, all preconditions and inputs required for the test are set up. This includes initializing objects, creating mock dependencies, and defining expected outcomes. The goal is to prepare the environment so that the "Act" step can be performed.
Act: This section contains the action being tested. It should ideally consist of a single line of code that invokes the method or function on the unit under test. This is the focal point of the test.
Assert: In the final section, the outcome of the "Act" step is verified. This involves one or more assertion statements that check whether the results—such as return values, object state changes, or mock interactions—match the expectations defined in the "Arrange" section.

Visually separating these three sections with comments or simple line breaks further improves readability, making it easy for a developer to scan the test and understand its flow.

Here is a C# example demonstrating the AAA pattern:

C#

public void Remove_ASubstring_RemovesThatSubstring()
{
// Arrange
var stringManipulator = new StringManipulator("Hello, world!");
var substringToRemove = "Hello";
var expectedResult = ", world!";

// Act
var actualResult = stringManipulator.Remove(substringToRemove);

// Assert
Assert.AreEqual(expectedResult, actualResult);
}

Strategic Use of Setup and Teardown Fixtures

Test fixtures are mechanisms used to manage the state of the test environment. They consist of setup code that runs before a test (or group of tests) and teardown code that runs after, ensuring a clean and predictable state for each test execution.

When to Use Fixtures: Test setup methods (e.g., `` in NUnit, @beforeEach in Jest, or @pytest.fixture in Pytest) are useful for handling repetitive Arrange logic that is common across many tests within the same class or module. For example, if multiple tests require an instance of the same complex object, creating it in a setup fixture can reduce code duplication.
When to Avoid Fixtures: While fixtures can promote code reuse, they must be used with caution. Over-reliance on setup fixtures can obscure important context from the body of the test, making it difficult to understand what is being tested without cross-referencing another method. This violates the DAMP (Descriptive and Meaningful Phrases) principle, which prioritizes clarity even at the cost of some repetition. For tests that have unique setup requirements, it is far clearer to perform the setup inline within the test method itself.

The choice between inline setup, dedicated helper methods, and framework-provided fixtures represents a fundamental design trade-off in testing. It is a tension between the DRY (Don't Repeat Yourself) principle, which aims to eliminate redundancy, and the DAMP principle, which prioritizes readability and clarity. Google's engineering philosophy explicitly warns against applying DRY too rigidly in tests, as it can lead to brittle abstractions that are hard to understand and maintain. Microsoft's guidance echoes this sentiment, suggesting that simple helper methods are often preferable to

SetUp attributes because they keep all relevant code visible within the test and reduce the risk of creating unwanted shared state between tests.

Therefore, the expert recommendation is not to use fixtures to eliminate all duplication blindly. Instead, teams should prioritize clarity. Fixtures are best reserved for genuinely boilerplate, non-critical setup code. Any setup logic that is directly relevant to the specific behavior being tested should be made explicit within the test method or in a clearly named helper function called from the test. This nuanced approach is critical for ensuring the long-term health and maintainability of a test suite.

Common Pitfalls: Anti-Patterns to Avoid

While following best practices is crucial, it is equally important to recognize and avoid common anti-patterns. These anti-patterns are seductive because they often appear to be shortcuts that save time in the short term. However, they introduce fragility, complexity, and unreliability into the test suite, creating a significant maintenance burden over the long term. The true cost of these shortcuts is paid in future developer velocity and confidence.

1) The Peril of Infrastructure Dependencies

One of the most severe anti-patterns is allowing a unit test to have dependencies on external infrastructure. This includes databases, network services, file systems, or any other component that lives outside the process of the test runner.

Such dependencies violate the core principle of isolation and introduce several problems:

Slowness: Interacting with a network or database is orders of magnitude slower than in-memory operations, causing the test suite to become sluggish.
Brittleness and Non-Determinism: The test can fail for reasons entirely unrelated to the code under test, such as a network timeout, a database deadlock, or a change in external data. This makes the test unreliable.

Tests that require real infrastructure are not unit tests; they are integration tests. These tests are valuable but should be separated from the unit test suite and run less frequently, as they serve a different purpose.

2) Avoiding Logic in Test Code

A unit test should be simple, straightforward, and easily verifiable by inspection. It should not contain its own complex logic, such as loops (for, while), conditional statements (if, switch), or other intricate operations.

Introducing logic into a test is highly problematic for two reasons:

It introduces the possibility of a bug in the test itself. A buggy test provides no value; it can either fail for the wrong reason or, even worse, pass incorrectly, giving a false sense of security.
It makes the test difficult to understand. The purpose of a test should be immediately obvious. Complex logic obscures the test's intent and makes it harder to debug when it fails.

If a test seems to require logic, it is often a "test smell" indicating that it is trying to do too much. The best solution is to split the test into multiple, simpler tests, each focused on a single behavior. For scenarios that require testing multiple data variations of the same behavior, frameworks provide

parameterized tests, which are a clean, declarative alternative to writing a loop inside a test.

3) The Dangers of "Magic Strings" and Brittle Values

"Magic strings" or "magic numbers" are unexplained, hard-coded literal values used within a test. They make the test difficult to read because the significance of the value is not immediately clear.

For example, consider the following assertion:

C#
// Bad: What does 86400 represent?
Assert.AreEqual(86400, result);

This code forces the reader to guess the meaning of the number. A much better approach is to assign the value to a well-named constant that expresses its intent. This practice makes the test self-documenting and easier to maintain.

C#
// Good: The intent is clear.
const int SECONDS_IN_A_DAY = 86400;
Assert.AreEqual(SECONDS_IN_A_DAY, result);

This principle of avoiding unexplained values is critical for maintaining a clean and understandable test suite. The cost of these anti-patterns accumulates over time, creating a form of technical debt within the test suite itself. This debt directly mortgages future development velocity for a minor, short-term convenience.

For instance, Google's internal analysis of "Change-Detector Tests"—tests that are tightly coupled to implementation details and break on any refactoring—is a prime example of this trade-off. Such tests are easy to write but provide negative value over time by creating maintenance churn without effectively catching bugs. Engineering leaders must therefore champion practices that prioritize long-term sustainability over short-term shortcuts.

How Can You Ensure Comprehensive Validation?

A high-quality test suite provides comprehensive validation of the code's behavior. This requires more than just testing the most common scenarios. It involves systematically exploring boundary conditions, writing precise and meaningful assertions, and using coverage metrics as a guide for improvement. These three elements—edge cases, assertions, and coverage—form a "three-legged stool" for test quality; a weakness in any one area compromises the entire structure.

Testing Happy Paths, Edge Cases, and Failure Scenarios

A robust test suite must cover a spectrum of scenarios beyond the "happy path," which represents the expected, normal usage of a piece of code.

Happy Path: This is the starting point for testing. It verifies that the code works correctly under ideal conditions with typical inputs.
Edge Cases: These are tests that probe the boundaries and extremes of valid inputs. Testing edge cases is critical for uncovering subtle bugs that occur at the limits of a component's operating parameters. Examples include:
- Numeric Boundaries: Zero, negative numbers, maximum integer values (int.MaxValue), minimum values. For a function that accepts a number between 50 and 100, the edge cases are precisely 50 and 100.
- String Boundaries: Empty strings (""), strings with only whitespace, very long strings, strings with special characters.
- Null and Empty Collections: null inputs, empty arrays or lists.
Failure Cases (Negative Tests): These tests verify that the code behaves correctly when it receives invalid input. This often means asserting that the code throws the expected type of exception. For example, if a method should throw an ArgumentNullException when passed a null value, a dedicated test should be written to ensure this behavior occurs.

Writing Meaningful Assertions Focused on State and Behavior

Assertions are the heart of a unit test—they are the statements that perform the actual check. For a test to be valuable, its assertions must be meaningful, precise, and focused on the right things.

Assert State, Not Interactions: A key best practice, emphasized in Google's engineering guides, is to favor asserting the final state of an object over verifying the interactions (i.e., the specific sequence of method calls) that led to that state. State-based tests are generally less brittle because they are coupled to the "what" (the outcome) rather than the "how" (the implementation). Interaction-based tests, which often rely heavily on mocking frameworks, can break easily during refactoring, even if the code's external behavior remains correct.
Use Specific Assertions: Modern testing frameworks provide a rich library of assertion methods. Developers should always use the most specific assertion available for the task. For example, instead of Assert.AreEqual(true, list.Contains(item)), use a more expressive method like Assert.Contains(item, list) (in NUnit) or expect(list).toContain(item) (in Jest). Specific assertions provide much clearer and more helpful failure messages.
The "One Assert Per Test" Principle (Conceptually): While a test method can contain multiple physical Assert statements, they should all work together to verify a single logical concept or behavior. If a test starts asserting multiple, unrelated facts, it's a sign that it has lost focus and should be split into separate, more targeted tests.

A Pragmatic Approach to Test Coverage Metrics

Code coverage is a quantitative metric that measures the percentage of an application's source code that is executed by its automated tests. It is a useful tool for identifying untested parts of a codebase, but it must be interpreted with caution.

Line Coverage vs. Branch Coverage:
- Line Coverage: This is the simplest metric. It measures the percentage of executable lines of code that were run during testing. While easy to understand, it can be misleading.
- Branch Coverage: This is a more sophisticated and meaningful metric. It measures the percentage of decision branches in the code that have been executed. For every if statement, it checks whether both the true and false paths were taken. A piece of code can have 100% line coverage but only 50% branch coverage, which means a critical scenario has been completely missed by the tests.
Coverage as a Tool, Not a Target: The most critical thing to understand about code coverage is that it is a tool for discovery, not a measure of quality. High coverage does not guarantee good tests. It is trivial to write tests that execute every line of code but have no meaningful assertions, thereby achieving 100% coverage while providing zero actual validation.

The proper way to use coverage is to analyze the reports to find critical areas of the application that are not tested. It helps answer the question, "What important logic have we forgotten to test?" rather than serving as a performance metric to be blindly chased. For most teams, aiming for a pragmatic goal of 80-90%

branch coverage is a far healthier and more effective strategy than demanding a dogmatic 100% line coverage.

How Can You Master Test-Driven Development (TDD)?

Test-Driven Development (TDD) is a software development process that inverts the traditional "code first, test later" workflow. In TDD, the test is written before the production code that it validates. While it may seem counterintuitive at first, TDD is a powerful discipline that leads to higher-quality code and more robust, emergent design.

The Red-Green-Refactor Cycle Explained

TDD operates on a short, iterative cycle known as "Red-Green-Refactor." This cycle, which can be as brief as 30 seconds for each small piece of functionality, ensures that the codebase is always in a working, tested state.

Red - Write a Failing Test: The developer begins by writing a single, small unit test for a new piece of functionality. Since the production code for this feature does not yet exist, this test is expected to fail (or not even compile). The failing state is often represented by the color red in test runners. This step forces the developer to clearly define the requirements and desired behavior of the new code before writing it.
Green - Write Code to Pass the Test: Next, the developer writes the absolute minimum amount of production code necessary to make the failing test pass. The goal is not to write perfect or complete code, but simply to satisfy the contract defined by the test. When the test passes, the test runner shows green.
Refactor - Improve the Code: With the safety of a passing test, the developer can now confidently refactor and clean up the code that was just written. This is the step where the implementation is improved, duplication is removed, and the design is polished, all while continuously re-running the test to ensure that no functionality was broken.

This cycle is then repeated for the next piece of functionality, gradually building up the application feature by feature, with a comprehensive test suite growing alongside it.

How TDD Fosters Emergent Design and Clean Code

The primary benefit of TDD is often misunderstood. It is not fundamentally a testing technique; it is a design technique. The resulting test suite is a valuable artifact, but the true prize is the quality of the production code architecture that emerges from the TDD process.

Consumer-First Perspective: TDD forces developers to think about their code from the perspective of a client or consumer first. Before considering implementation details, they must ask, "How will this code be used? What should its API look like?" This leads to cleaner, more intuitive, and more usable interfaces.
Testability by Design: Because every piece of code is written to satisfy a test, it must be inherently testable. This naturally pushes developers toward good design principles like high cohesion, low coupling, and the use of dependency injection, as tightly coupled code is difficult to test in isolation.
Continuous Refactoring: The "Refactor" step is a formal, non-negotiable part of the TDD cycle. This ensures that design improvements and code cleanup are continuous activities, not an afterthought that gets relegated to a "technical debt" backlog. This keeps the codebase clean and maintainable as it evolves.

Integrating TDD into Your Workflow

TDD is a core practice in many Agile development methodologies, as its iterative nature and rapid feedback loops align perfectly with the principles of Agile.

For teams new to TDD, it is best to start small. Pick a simple, well-defined feature to practice the Red-Green-Refactor rhythm. Modern development tools and frameworks are often designed to support a TDD workflow. For example, JavaScript testing frameworks like Jest include a "watch mode" that automatically re-runs the relevant tests every time a code file is saved. This tightens the feedback loop to mere seconds, making the TDD cycle fluid and efficient.

Teams that view TDD as merely "writing tests first" miss its profound impact on software design. The constraints of the TDD cycle guide developers toward creating simple, decoupled, and highly maintainable systems. The comprehensive test suite is a valuable byproduct of this design-centric process.

How Should Testing Be Integrated into the Development Lifecycle?

Effective unit testing is not an isolated activity performed at the end of a development cycle. To realize its full benefits, it must be deeply integrated into the daily workflow of the engineering team and automated within the software delivery pipeline. This integration is a cornerstone of modern DevOps and Agile practices.

Shift-Left: The Power of Early and Continuous Testing

The "shift-left" movement in software development refers to the practice of moving testing activities earlier in the development lifecycle—shifting them to the "left" on a typical project timeline. Unit testing is the ultimate embodiment of this principle. Instead of waiting for a dedicated QA phase, developers write and execute unit tests concurrently with the production code.

This proactive approach provides an immediate feedback loop, allowing defects to be found and fixed when they are cheapest and easiest to resolve. By catching bugs at their source, shift-left testing prevents them from propagating into more complex parts of the system, where they become exponentially more difficult and costly to diagnose and repair.

Automating Quality Gates with Continuous Integration (CI)

Unit tests form the foundation of any modern Continuous Integration (CI) and Continuous Delivery (CD) pipeline. CI is the practice where developers frequently merge their code changes into a central repository, after which automated builds and tests are run.

The process typically works as follows:

A developer commits a code change to the version control system.
The CI server (e.g., Jenkins, CircleCI, GitHub Actions) automatically detects the change.
The server triggers a new build of the application.
Immediately following the build, the entire automated unit test suite is executed.

This automated execution of unit tests acts as a quality gate. If any test fails, the build is marked as "broken," and the team is immediately notified. This prevents regressions—bugs introduced into previously working code—from being merged into the main codebase and affecting other team members.

The impact of this integration is significant. According to a report on software testing, teams with strong test automation and CI integration report both faster release cycles (86% of teams) and reduced defect leakage into production (71% of teams).

CI without a fast, reliable, and comprehensive automated test suite is merely "continuous integration theater." It automates the build process—confirming that the code compiles—but provides no actual assurance of quality or correctness. The unit test suite is the engine that powers a meaningful CI/CD pipeline. Therefore, investing in CI infrastructure without a parallel, dedicated investment in building and maintaining a high-quality test suite will fail to deliver the promised benefits of speed and stability. The test suite must be treated as a first-class citizen of the CI/CD process, not as an optional add-on.

How Do You Maintain Test Suite Health for the Long Term?

A unit test suite is not a "write-once, forget-forever" artifact. It is a living part of the codebase and, like production code, is subject to entropy and the accumulation of technical debt. To ensure that a test suite remains a valuable asset rather than a maintenance liability, it requires active, ongoing care and attention.

Identifying and Eliminating "Test Smells"

Just as "code smells" indicate potential problems in production code, "test smells" are symptoms of poor design in test code that can make the suite difficult to understand, maintain, and trust. Recognizing and addressing these smells is a key part of maintaining test suite health.

Common test smells include:

Excessive Setup: A test that requires hundreds of lines of setup code is a sign that the unit under test may have too many dependencies or that the test is not properly focused.
Complex Logic: As discussed previously, tests containing loops, conditionals, or other logic are a major smell.
Flaky Tests: Tests that are non-deterministic and fail intermittently erode trust in the entire suite.
Tight Coupling to Implementation: Tests that verify private methods or internal implementation details are brittle and will break unnecessarily during refactoring.
Assertion Roulette: A test with many assertions that provides a generic failure message, forcing the developer to debug the test to understand what actually failed.

Recent research highlights the significance of this problem, with new tools like UTRefactor using Large Language Models (LLMs) to automatically detect and refactor test smells, demonstrating the industry's focus on improving test code quality.

Refactoring Tests for Clarity and Maintainability

Tests must be refactored and maintained with the same rigor as production code. As the application evolves, the test suite must evolve with it to remain relevant and effective.

Test refactoring involves activities such as:

Improving Naming: Renaming tests and variables to better reflect their intent as the system's domain language evolves.
Removing Duplication: Consolidating redundant setup logic into well-structured helper methods or fixtures, while being mindful of the DRY vs. DAMP trade-off.
Simplifying Assertions: Breaking down complex assertions into simpler, more focused checks to improve failure diagnostics.
Deleting Obsolete Tests: Removing tests that are no longer relevant or that test functionality that has been deprecated.

The goal of test maintenance is to ensure the test suite remains a healthy, reliable safety net that enables change rather than impeding it. Engineering teams should budget time for "test maintenance" as a regular, planned activity, just as they do for maintaining production code. Introducing "test health" as a recurring topic in sprint retrospectives can help formalize this practice and prevent the test suite from decaying over time.

Unit Testing Best Practices: Glossary Table

This table provides a high-density, scannable summary of the key unit test best practices and their strategic importance. It serves as a quick reference and a checklist for teams seeking to adopt and reinforce these principles.

Practice	Why It Matters
Test automation	Speeds feedback and consistency
Test coverage	Focuses on critical code paths (especially branch coverage)
Mocking dependencies	Enables isolation and predictability
Test isolation	Prevents cross-test interference and ensures reliable results
Fast execution	Encourages frequent runs and provides rapid feedback
Repeatability	Builds trust in test results
Clear naming conventions	Helps readability and troubleshooting
Small test cases	Eases debugging and maintenance by testing one behavior
Setup & teardown	Keeps tests clean and predictable, manages state
Assertions correctness	Ensures meaningful validation of behavior, not implementation
Early testing integration	Reduces debugging costs late in the cycle (Shift-Left)
TDD	Promotes testable design and clarity from the start
Refactoring tests	Keeps the test suite healthy and maintainable over time
Continuous integration	Automates quality checks and prevents regressions
Deterministic tests	Guarantees consistent outcomes and builds trust

This checklist distills the report's extensive analysis into a powerful, actionable artifact. By linking each practice to its core benefit, it elevates the discussion from a list of rules to a strategic overview of quality engineering.

Conclusion

The adoption of disciplined unit test best practices is not merely a technical exercise; it is a strategic investment in the long-term health and velocity of a software project. These practices work in concert to build a robust safety net that provides developers with the confidence to innovate, refactor, and respond to change with speed and agility. By focusing on isolation, clarity, speed, and maintainability, teams can transform their test suite from a costly afterthought into a powerful asset that drives quality, accelerates delivery, and serves as the foundation for a culture of engineering excellence.

The journey toward a mature testing culture can seem daunting, but it does not require an overnight transformation. The most effective approach is to start small and build momentum through iterative improvement.

Begin with New Code: Apply these principles rigorously to all new features and bug fixes.
Focus on One Practice: Pick one or two areas for immediate improvement, such as adopting a clear test naming convention or ensuring all new tests are properly isolated from infrastructure.
Build Habits: Like any aspect of software craftsmanship, building a culture of quality is an iterative process of forming good habits. By consistently applying these practices, teams can steadily improve their codebase, their processes, and their products.

FAQ Section

1) What are the best practices for unit tests?

The best practices for unit tests involve writing tests that are small, isolated, fast, repeatable, and deterministic. They should be named clearly using a convention like Method_Scenario_ExpectedBehavior, structured with the Arrange-Act-Assert (AAA) pattern, and avoid dependencies on external infrastructure like databases or networks. Additionally, a robust suite tests edge cases, integrates into a CI/CD pipeline for automated feedback, and is refactored over time to maintain its health.

2) Which three items are best practices for unit tests?

Three of the most critical best practices for unit tests are: 1) Test isolation, achieved by using mocks and stubs to eliminate external dependencies. 2) Clear and descriptive naming conventions, such as Method_Scenario_ExpectedBehavior, to make tests self-documenting. 3) Fast, deterministic, and repeatable execution, which ensures that tests provide reliable and rapid feedback, building trust in the test suite.

3) What are the 3 A’s of unit testing?

The 3 A's of unit testing are Arrange, Act, and Assert. This is a simple and effective pattern for structuring the body of a test to enhance clarity and readability.

Arrange: Set up all necessary preconditions and inputs.
Act: Execute the specific piece of code being tested.
Assert: Verify that the outcome of the action is correct.

4) How to unit test properly?

To unit test properly, focus on writing small, isolated tests that verify a single behavior. Use the Arrange-Act-Assert (AAA) pattern for structure and apply meaningful names for clarity. Employ mocks and stubs to isolate the unit from its dependencies. Write precise assertions that validate the outcome, not the implementation details. Avoid putting logic (like loops or conditionals) in your tests, ensure they are deterministic, and integrate them into a CI pipeline to run early and often.

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TL;DR: Secure Code Review Checklist

A secure code review checklist is a structured guide to ensure code is free from common security flaws before reaching production. The core items include:

Input Validation – Validate and sanitize all user input on the server side.
Output Encoding – Use context-aware encoding to prevent XSS.
Authentication & Authorization – Enforce server-side checks, hash & salt passwords, follow least privilege.
Error Handling & Logging – Avoid leaking sensitive info, log security-relevant events without secrets.
Data Encryption – Encrypt data at rest and in transit using strong standards (TLS 1.2+, AES-256).
Session Management – Secure tokens, timeouts, HttpOnly & Secure cookies.
Dependency Management – Use SCA tools, keep libraries updated.
Logging & Monitoring – Track suspicious activity, monitor alerts, protect log files.
Threat Modeling – Continuously validate assumptions and attack vectors.
Secure Coding Practices – Follow OWASP, CERT, and language-specific standards.

Use this checklist during manual reviews, supported by automation (SAST/SCA tools), to catch vulnerabilities early, reduce costs, and standardize secure development practices.

Why Use a Secure Code Review Checklist?

Code quality and vulnerability assessment are two sides of the same coin. A checklist provides a systematic approach to both. It helps standardize the review process across your entire team, ensuring no critical security checks are overlooked. This is why we use a secure code review checklist.

The primary benefit is catching security issues early in the development lifecycle. Fixing a vulnerability during development is significantly less costly and time-consuming than patching it in production. According to a report by the Systems Sciences Institute at IBM, a bug found in production is six times more expensive to fix than one found during design and implementation.

Organizations like the Open Web Application Security Project (OWASP) provide extensive community-vetted resources that codify decades of security wisdom. A checklist helps you put this wisdom into practice. Even if the checklist items seem obvious, the act of using one frames the reviewer's mindset, focusing their attention specifically on security concerns. This focus alone significantly increases the likelihood of detecting vulnerabilities that might otherwise be missed.

Standardization: Ensures every piece of code gets the same security scrutiny.
Efficiency: Guides reviewers to the most critical areas quickly.
Early Detection: Finds and fixes flaws before they become major problems.
Knowledge Sharing: Acts as a teaching tool for junior developers.

Preparing Your Secure Code Review

A successful review starts before you look at a single line of code. Proper preparation ensures your efforts are focused and effective. Without a plan, reviews can become unstructured and miss critical risks.

Threat Modeling First

Before reviewing code, you must understand the application's potential threats. Threat modeling is a process where you identify security risks and potential vulnerabilities.

Ask questions like:

Where does the application handle sensitive data?
What are the entry points for user input?
How do different components authenticate with each other?
What external systems does the application trust?

This analysis helps you pinpoint high-risk areas of the codebase architecture that demand the most attention.

Define Objectives

Clarify the goals of the review. Are you hunting for specific bugs, verifying compliance with a security standard, or improving overall code quality? Defining your objectives helps focus the review and measure its success.

Set Scope

You do not have to review the entire codebase at once. Start with the most critical and high-risk code segments identified during threat modeling.

Focus initial efforts on:

Authentication and Authorization Logic: Code that handles user logins and permissions.
Session Management: Functions that create and manage user sessions.
Data Encryption Routines: Any code that encrypts or decrypts sensitive information.
Input Handling: Components that process data from users or external systems.

Gather the Right Tools and People

Assemble a review team with a good mix of skills. Include the developer who wrote the code, a security-minded developer, and, if possible, a dedicated security professional. This combination of perspectives provides a more thorough assessment.

Equip the team with the proper tools, including access to the project's documentation and specialized software. For instance, static analysis tools can automatically scan for vulnerabilities. For threat modeling, you might use OWASP Threat Dragon, and for automation, a platform like GitHub Actions can integrate security checks directly into the workflow.

Core Secure Code Review Checklist Items

This section contains the fundamental items that should be part of any review. Each one targets a common area where security vulnerabilities appear.

1) Input Validation

Attackers exploit applications by sending malicious or unexpected input. Proper input validation is your first line of defense.

Validate on the Server Side: Never trust client-side validation alone. Attackers can easily bypass it. Always re-validate all inputs on the server.
Classify Data: Separate data into trusted (from internal systems) and untrusted (from users or external APIs) sources. Scrutinize all untrusted data.
Centralize Routines: Create and use a single, well-tested library for all input validation. This avoids duplicated effort and inconsistent logic.
Canonicalize Inputs: Convert all input into a standard, simplified form before processing. For example, enforce UTF-8 encoding to prevent encoding-based attacks.

2) Output Encoding

Output encoding prevents attackers from injecting malicious scripts into the content sent to a user's browser. This is the primary defense against Cross-Site Scripting (XSS).

Encode on the Server: Always perform output encoding on the server, just before sending it to the client.
Use Context-Aware Encoding: The method of encoding depends on where the data will be placed. Use specific routines for HTML bodies, HTML attributes, JavaScript, and CSS.
Utilize Safe Libraries: Employ well-tested libraries provided by your framework to handle encoding. Avoid writing your own encoding functions.

3) Authentication & Authorization

Authentication confirms a user's identity, while authorization determines what they are allowed to do. Flaws in these areas can give attackers complete control.

Enforce on the Server: All authentication and authorization checks must occur on the server.
Use Tested Services: Whenever possible, integrate with established identity providers or use your framework's built-in authentication mechanisms.
Centralize Logic: Place all authorization checks in a single, reusable location to ensure consistency.
Hash and Salt Passwords: Never store passwords in plain text. Use a strong, adaptive hashing algorithm like Argon2 or bcrypt with a unique salt for each user.
Use Vague Error Messages: On login pages, use generic messages like "Invalid username or password." Specific messages ("User not found") help attackers identify valid accounts.
Secure External Credentials: Protect API keys, database credentials, and other secrets. Store them outside of your codebase using a secrets management tool.

4) Error Handling & Logging

Proper error handling prevents your application from leaking sensitive information when something goes wrong.

Avoid Sensitive Data in Errors: Error messages shown to users should never contain stack traces, database queries, or other internal system details.
Log Sufficient Context: Your internal logs should contain enough information for debugging, such as a timestamp, the affected user ID (if applicable), and the error details.
Do Not Log Secrets: Ensure that passwords, API keys, session tokens, and other sensitive data are never written to logs.

5) Data Encryption

Data must be protected both when it is stored (at rest) and when it is being transmitted (in transit).

Encrypt Data in Transit: Use Transport Layer Security (TLS) 1.2 or higher for all communication between the client and server.
Encrypt Data at Rest: Protect sensitive data stored in databases, files, or backups.
Use Proven Standards: Implement strong, industry-accepted encryption algorithms like AES-256. For databases, use features like Transparent Data Encryption (TDE) or column-level encryption for the most sensitive fields.

6) Session Management & Access Controls

Once a user is authenticated, their session must be managed securely. Access controls ensure users can only perform actions they are authorized for.

Secure Session Tokens: Generate long, random, and unpredictable session identifiers. Do not include any sensitive information within the token itself.
Expire Sessions Properly: Sessions should time out after a reasonable period of inactivity. Provide users with a clear log-out function that invalidates the session on the server.
Guard Cookies: Set the Secure and HttpOnly flags on session cookies. This prevents them from being sent over unencrypted connections or accessed by client-side scripts.
Enforce Least Privilege: Users and system components should only have the minimum permissions necessary to perform their functions.

7) Dependency Management

Modern applications are built on a foundation of third-party libraries and frameworks. A vulnerability in one of these dependencies is a vulnerability in your application.

Use Software Composition Analysis (SCA) Tools: These tools scan your project to identify third-party components with known vulnerabilities.
Keep Dependencies Updated: Regularly update your dependencies to their latest stable versions. Studies from organizations like Snyk regularly show that a majority of open-source vulnerabilities have fixes available. A 2025 Snyk report showed projects using automated dependency checkers fix vulnerabilities 40% faster.

8) Logging & Monitoring

Secure logging and monitoring help you detect and respond to attacks in real-time.

Track Suspicious Activity: Log security-sensitive events such as failed login attempts, access-denied errors, and changes to permissions.
Monitor Logs: Use automated tools to monitor logs for patterns that could indicate an attack. Set up alerts for high-priority events.
Protect Your Logs: Ensure that log files are protected from unauthorized access or modification.

9) Threat Modeling

During the review, continuously refer back to your threat model. This helps maintain focus on the most likely attack vectors.

Review Data Flows: Trace how data moves through the application.
Validate Trust Boundaries: Pay close attention to points where the application interacts with external systems or receives user input.
Question Assumptions: Could an attacker manipulate this data flow? Could they inject code or bypass a security control?

10) Code Readability & Secure Coding Standards

Clean, readable code is easier to secure. Ambiguous or overly complex logic can hide subtle security flaws.

Write Clear Code: Use meaningful variable names, add comments where necessary, and keep functions short and focused.
Use Coding Standards: Adhere to established secure coding standards for your language. Some great resources are the OWASP Secure Coding Practices, the SEI CERT Coding Standards, and language-specific guides.

11) Secure Data Storage

How and where you store sensitive data is critical. This goes beyond just encrypting the database.

Protect Backups: Ensure that database backups are encrypted and stored in a secure location with restricted access.
Sanitize Data: When using production data in testing or development environments, make sure to sanitize it to remove any real user information.
Limit Data Retention: Only store sensitive data for as long as it is absolutely necessary. Implement and follow a clear data retention policy.

Automated Tools to Boost Your Checklist

Manual reviews are essential for understanding context and business logic, but they can be slow and prone to human error. For smaller teams, free and open-source tools like SonarQube, Snyk, and Semgrep perfectly complement a manual secure code review checklist by catching common issues quickly and consistently.

Integrate SAST and SCA into CI/CD

Integrate Static Application Security Testing (SAST) and Software Composition Analysis (SCA) tools directly into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. This automates the initial security scan on every code commit.

SAST Tools: These tools analyze your source code without executing it. They are excellent at finding vulnerabilities like SQL injection, buffer overflows, and insecure configurations.
SCA Tools: These tools identify all the open-source libraries in your codebase and check them against a database of known vulnerabilities.

Configure Security-Focused Rules

Configure your automated tools to enforce specific security rules tied to standards like OWASP Top 10 or the SEI CERT standards. This ensures that the automated checks are directly connected to your security requirements.

Popular Static Analysis Tools

Several tools can help automate parts of your review:

PVS-Studio: A static analyzer for C, C++, C#, and Java code.
Semgrep: A fast, open-source static analysis tool that supports many languages and allows for custom rules.
SonarQube: An open-platform to manage code quality, which includes security analysis features.

Running The Review

With your preparation complete and checklist in hand, it is time to conduct the review. A structured approach makes the process more efficient and less draining for the participants.

Timebox Your Sessions

Limit each review session to about 60-90 minutes. Longer sessions can lead to fatigue and reduced focus, making it more likely that reviewers will miss important issues. It is better to have multiple short, focused sessions than one long, exhaustive one.

Apply the Checklist Systematically

Work through your checklist steadily. Start with the high-risk areas you identified during threat modeling. Use a combination of automated tools and manual inspection.

Run Automated Scans First: Let SAST and SCA tools perform an initial pass to catch low-hanging fruit.
Manually Inspect High-Risk Code: Use your expertise and the checklist to examine authentication, authorization, and data handling logic.
Validate Business Logic: Check for flaws in the application's logic that an automated tool would miss.

Track Metrics for Improvement

To make your process repeatable and measurable, track key metrics.

Metric	Description	Purpose	Tracking Tools
Inspection Rate	Lines of code reviewed per hour.	Helps in planning future reviews.	Code review systems (Crucible, Gerrit) or custom dashboards (Grafana, Tableau) pulling data from version control.
Defect Density	Number of defects found per 1,000 lines of code.	Measures code quality over time.	Static analysis tools (SonarQube) and issue trackers (Jira, GitHub Issues).
Time to Remediate	Time taken to fix a reported issue.	Measures the efficiency of your response process.	Issue trackers like Jira, GitHub Issues, Asana, or service desk software like Zendesk.

Keeping Your Process Up to Date

Security is not a one-time activity. The threat environment is constantly changing, and your review process must adapt. An effective secure code review checklist is a living document.

Update for New Threats

Regularly review and update your checklist to include checks for new types of vulnerabilities. Stay informed by following security publications from organizations like NIST and OWASP. When a new major vulnerability is disclosed (like Log4Shell), update your checklist to include specific checks for it.

Build a Security-First Mindset

The ultimate goal is to create a team where everyone thinks about security. Use the code review process as an educational opportunity. When you find a vulnerability, explain the risk and the correct way to fix it. This continuous training builds a stronger, more security-aware engineering team.

Sample “Starter” Checklist

Here is a starter secure code review checklist based on the principles discussed. You can use this as a foundation and customize it for your specific tech stack and application. This is structured in a format you can use in a GitHub pull request template.

For a more detailed baseline, the OWASP Code Review Guide and the associated Quick Reference Guide are excellent resources.

Input Validation

[Critical] Is the application protected against injection attacks (SQLi, XSS, Command Injection)?
[Critical] Is all untrusted input validated on the server side?
[High] Is input checked for length, type, and format?
[Medium] Is a centralized input validation routine used?

Authentication & Authorization

[Critical] Are all sensitive endpoints protected with server-side authentication checks?
[Critical] Are passwords hashed using a strong, salted algorithm (e.g., Argon2, bcrypt)?
[Critical] Are authorization checks performed based on the user's role and permissions, not on incoming parameters?
[High] Are account lockout mechanisms in place to prevent brute-force attacks?
[High] Does the principle of least privilege apply to all user roles?

Session Management

[Critical] Are session tokens generated with a cryptographically secure random number generator?
[High] Are session cookies configured with the HttpOnly and Secure flags?
[High] Is there a secure log-out function that invalidates the session on the server?
[Medium] Do sessions time out after a reasonable period of inactivity?

Data Handling & Encryption

[Critical] Is all sensitive data encrypted in transit using TLS 1.2+?
[High] Is sensitive data encrypted at rest in the database and in backups?
[High] Are industry-standard encryption algorithms (e.g., AES-256) used?
[Medium] Are sensitive data or system details avoided in error messages?

Dependency Management

[High] Has an SCA tool been run to check for vulnerable third-party libraries?
[High] Are all dependencies up to their latest secure versions?

Logging & Monitoring

[Critical] Are secrets (passwords, API keys) excluded from all logs?
[Medium] Are security-relevant events (e.g., failed logins, access denials) logged?

Conclusion

Building secure software requires a deliberate and systematic effort. This is why your team needs a secure code review checklist. It provides structure, consistency, and a security-first focus to your development process. It transforms code review from a simple bug hunt into a powerful defense against attacks.

For the best results, combine the discipline of a powerful secure code review checklist with automated tools and the contextual understanding that only human reviewers can provide. This layered approach ensures you catch a wide range of issues, from simple mistakes to complex logic flaws. Begin integrating these principles and build your own secure code review checklist today. Your future self will thank you for the secure and resilient applications you create.

FAQs

1) What are the 7 steps to review code?

A standard secure code review process involves seven steps:

Define review goals and scope.
Gather the code and related artifacts.
Run automated SAST/SCA tools for an initial scan.
Perform a manual review using a checklist, focusing on high-risk areas.
Document all findings clearly with actionable steps.
Prioritize the documented issues based on risk.
Remediate the issues and verify the fixes.

2) How to perform a secure code review?

To perform a secure code review, you should first define your objectives and scope, focusing on high-risk application areas. Then, use a checklist to guide your manual inspection, and supplement your review with SAST and SCA tools. Document your findings and follow up to ensure fixes are correctly implemented.

3) What is a code review checklist?

A secure code review checklist is a structured list of items that guides a reviewer. It ensures consistent and thorough coverage of critical security areas like input validation, authentication, and encryption, helping to prevent common vulnerabilities and avoid gaps in the review process.

4) What are SAST tools during code review?

SAST stands for Static Application Security Testing. These tools automatically scan an application's source code for known vulnerability patterns without running the code. Tools like PVS-Studio, Semgrep, or SonarQube can find potential issues such as SQL injection, buffer overflows, and insecure coding patterns early in development.

5) How long should a secure code review take per 1,000 LOC?

There isn't a strict time rule, as the duration depends on several factors. However, a general industry guideline for a manual review is between 1 to 4 hours per 1,000 lines of code (LOC).

Factors that influence this timing include:

Code Complexity: Complex business logic or convoluted code will take longer to analyze than simple, straightforward code.
Reviewer's Experience: A seasoned security professional will often be faster and more effective than someone new to code review.
Programming Language: Some languages and frameworks have more inherent security risks and require more scrutiny.
Scope and Depth: A quick check for the OWASP Top 10 vulnerabilities is much faster than a deep, architectural security review.

LLM & Gen AI

Shivam Agarwal

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Code Dependencies: What They Are and Why They Matter

Dependencies in code are like ingredients for a recipe. When baking a cake, you don't grow the wheat and grind your own flour; you purchase it ready-made. Similarly, developers use pre-written code packages, known as libraries or modules, to construct complex applications without writing every single line from scratch.

These pre-made components are dependencies—external or internal pieces of code your project needs to function correctly. Managing them properly impacts your application's quality, security, and performance. When you build software, you integrate these parts created by others, which introduces a reliance on that external code. Your project's success is tied to the quality and maintenance of these components.

This article provides a detailed look into software dependencies. We will cover what they are, the different types you will encounter, and why managing them is a critical skill for any engineering team. We will also present strategies and tools to handle them effectively.

What “Dependency” Really Means in Programming

In programming, a dependency is a piece of code that your project relies on to function. These are often external libraries or modules that provide specific functionality. Think of them as pre-built components you use to add features to your application.

In software development, it's useful to distinguish between the general concept of dependence and the concrete term dependency.

Dependence is the state of relying on an external component for your code to function. It describes the "need" itself.
A dependency is the actual component you are relying on, such as a specific library, package, or framework.

This dependence means a change in a dependency can affect your code. For instance, if a library you use is updated or contains a bug, it directly impacts your project because of this reliance. Recognizing this is a foundational principle in software construction.

Libraries, External Modules, and Internal Code

It's useful to differentiate between a few common terms:

Software Libraries: These are collections of pre-written code that developers can use. For example, a library like NumPy in Python might offer functions for complex mathematical calculations. You import the library and call its functions.
External Modules: This is a similar concept. An external module is a self-contained unit of code that exists outside your primary project codebase. Package managers install these modules for you to use. A well-known example is React, which is used for building user interfaces.
Internal Modular Code: These are dependencies within your own project. You might break your application into smaller, reusable modules. For instance, a userAuth.js module could be used by both the authentication and profile sections of your application, creating an internal dependency.

A Community Perspective

Developers often use analogies to explain this concept. One clear explanation comes from a Reddit user, who states: “Software dependencies are external things your program relies on to work. Most commonly this means other libraries.” This simple definition captures the core idea perfectly.

Another helpful analogy from the same discussion simplifies it further: “...you rely on someone else to do the actual work and you just depend on it.” This highlights the nature of using a dependency. You integrate its functionality without needing to build it yourself.

Types of Code Dependencies: An Organized Look

Dependencies come in several forms, each relevant at different stages of the development lifecycle. Understanding these types helps you manage your project's architecture and build process more effectively. Knowing what are dependencies in code involves recognizing these distinct categories.

Common Dependency Categories

Here is a look at the most common types of dependencies you will work with.

Library Dependencies: These are the most common type. They consist of third-party code you import to perform specific tasks. Examples include react for building user interfaces or pandas for data manipulation in Python.
External Modules: This is a broad term for any code outside your immediate project. It includes libraries, frameworks, and any other packages you pull into your tech stack from an external registry.
Internal (Modular) Dependencies: These exist inside your project's codebase. When you structure your application into distinct modules, one module might require another to function. This creates a dependency between internal parts of your code.
Build Dependencies: These are tools required to build or compile your project. They are not needed for the final application to run, but they are essential during the development and compilation phase. A code transpiler like Babel is a classic example.
Compile-time Dependencies: These are similar to build dependencies. They are necessary only when the code is being compiled. For example, a C++ project might depend on header files that are not needed once the executable is created.
Runtime Dependencies: These are required when the application is actually running. A database connector, for instance, is a runtime dependency. The application needs it to connect to the database and execute queries in the production environment.

Transitive Dependencies

A critical concept is the transitive or indirect dependency. These are the dependencies of your dependencies. If your project uses Library A, and Library A uses Library B, then your project has a transitive dependency on Library B.

It's useful to distinguish this from a runtime dependency, which is any component your application needs to execute correctly in a live environment. While the two concepts often overlap, they are not identical.

Practical Example

Imagine you're building a web application using Node.js:

Direct Dependency: You add a library called Auth-Master to your project to handle user logins. Auth-Master is a direct dependency.
Transitive Dependency: Auth-Master requires another small utility library, Token-Gen, to create secure session tokens. You didn't add Token-Gen yourself, but your project now depends on it transitively.
Runtime Dependency: For the application to function at all, it must be executed by the Node.js runtime environment. Node.js is a runtime dependency. In this case, both Auth-Master and Token-Gen are also runtime dependencies because they are needed when the application is running to manage logins.

This illustrates that a component (Token-Gen) can be both transitive and runtime. The key difference is that "transitive" describes how you acquired the dependency (indirectly), while "runtime" describes when you need it (during execution).

These can become complex and are a major source of security vulnerabilities and license conflicts. According to the 2025 Open Source Security and Risk Analysis (OSSRA) report, 64% of open source components in applications are transitive dependencies. This shows how quickly they can multiply within a project. The tech publication DEV also points out the importance of tracking external, internal, and transitive dependencies to maintain a healthy codebase.

Why Code Dependencies Matter (and Why You Should Care)

Effective dependency management is not just an administrative task; it is central to building reliable, secure, and high-performing software. Neglecting them can introduce significant risks into your project.

Imagine a team launching a new feature, only to have the entire application crash during peak hours. After a frantic investigation, the culprit was identified: an unpatched vulnerability in an old third-party library. A simple version update, made months ago by the library's author, would have prevented the entire outage. Examining what are dependencies in code shows their direct link to project health.

1. Code Quality & Maintenance

Understanding dependencies is fundamental to good software architecture. It helps you structure code logically and predict the impact of changes. When one part of the system is modified, knowing what depends on it prevents unexpected breakages.

As the software analysis platform CodeSee explains it: “When Module A requires … Module B … we say Module A has a dependency on Module B.” This simple statement forms the basis of dependency graphs, which visualize how different parts of your code are interconnected, making maintenance much more predictable.

2. Security

Dependencies are a primary vector for security vulnerabilities. When you import a library, you are also importing any security flaws it may contain. Malicious actors frequently target popular open-source libraries to launch widespread attacks.

The threat is significant. According to the 2025 OSSRA report, a staggering 86% of audited applications contained open source vulnerabilities. The National Institute of Standards and Technology (NIST) provides extensive guidance on software supply chain security, recommending continuous monitoring and validation of third-party components as a core practice. Properly managing your dependencies is your first line of defense.

3. Performance

The performance of your application is directly tied to its dependencies. A slow or resource-intensive library can become a bottleneck, degrading the user experience. Large dependencies can also increase your application's bundle size, leading to longer load times for web applications.

By analyzing your dependencies, you can identify which ones are contributing most to performance issues. Sometimes, replacing a heavy library with a more lightweight alternative or writing a custom solution can lead to significant performance gains. This optimization is impossible without a clear picture of your project's dependency tree.

4. Legal & Licensing

Every external dependency you use comes with a software license. These licenses dictate how you can use, modify, and distribute the code. Failing to comply with these terms can lead to serious legal consequences.

License compatibility is a major concern. For example, using a library with a "copyleft" license (like the GPL) in a proprietary commercial product may require you to open-source your own code. The 2025 OSSRA report found that 56% of audited applications had license conflicts, many of which arose from transitive dependencies. Tools mentioned by DEV are essential for tracking and ensuring license compliance.

Managing Code Dependencies Like a Pro

Given their impact, you need a systematic approach to managing dependencies. Modern development relies on a combination of powerful tools and established best practices to keep dependencies in check. Truly understanding what are dependencies in code means learning how to control them.

a. Dependency Management Tools

Package managers are the foundation of modern dependency management. They automate the process of finding, installing, and updating libraries. Each major programming ecosystem has its own set of tools.

npm (Node.js): The default package manager for JavaScript. It manages packages listed in a package.json file.
pip (Python): Used to install and manage Python packages. It typically works with a requirements.txt file.
Maven / Gradle (Java): These are build automation tools that also handle dependency management for Java projects.
Yarn / pnpm: Alternatives to npm that offer improvements in performance and security for managing JavaScript packages.

These tools streamline the installation process and help resolve version conflicts between different libraries.

b. Virtual Environments

A virtual environment is an isolated directory that contains a specific version of a language interpreter and its own set of libraries. This practice prevents dependency conflicts between different projects on the same machine.

For example, Project A might need version 1.0 of a library, while Project B needs version 2.0. Without virtual environments, installing one would break the other. DEV details tools like pipenv and Poetry for Python, which create these isolated environments automatically. For Node.js, nvm (Node Version Manager) allows you to switch between different Node.js versions, each with its own global packages.

c. Semantic Versioning

Semantic Versioning (SemVer) is a versioning standard that provides meaning to version numbers. A version is specified as MAJOR.MINOR.PATCH.

MAJOR version change indicates an incompatible API change.
MINOR version change adds functionality in a backward-compatible manner.
PATCH version change makes backward-compatible bug fixes.

As noted by CodeSee, adhering to SemVer is crucial. It allows you to specify version ranges for your dependencies safely. For instance, you can configure your package manager to accept any new patch release automatically but require manual approval for a major version update that could break your code.

d. Visualization & Analysis Tools

For complex projects, it can be difficult to see the full dependency tree. This is where visualization and analysis tools come in.

Software Composition Analysis (SCA) Tools: These tools scan your project to identify all open-source components, including transitive dependencies. They check for known security vulnerabilities and potential license conflicts. The OWASP Dependency-Check project is a well-known open-source SCA tool.
Dependency Graph Visualizers: Tools like CodeSee's dependency maps can generate interactive diagrams of your codebase. These visualizations help you understand how modules interact and identify areas of high complexity or tight coupling.

e. Refactoring for Modularity

The best way to manage dependencies is to design a system with as few of them as needed. This involves writing modular code with clean interfaces. Principles like SOLID encourage loose coupling, where components are independent and interact through stable APIs.

A benefit of modular programming is that it makes code more reusable and easier to maintain. Research from educational resources on software design confirms that breaking down a system into independent modules improves readability and simplifies debugging. When you need to change one module, the impact on the rest of the system is minimized, which is a core goal of good dependency management.

Real-World Example in OOP

Object-Oriented Programming (OOP) provides a clear illustration of dependency principles. Improper dependencies between classes can make a system rigid and difficult to maintain. This example shows why thinking about what are dependencies in code is so important at the architectural level.

Imagine two classes in an HR system: Employee and HR.

Java
// A simple Employee class
public class Employee {
private String employeeId;
private String name;
private double salary;

// Constructor, getters, and setters
public Employee(String employeeId, String name, double salary) {
this.employeeId = employeeId;
this.name = name;
this.salary = salary;
}

public double getSalary() {
return salary;
}
}

// The HR class depends directly on the Employee class
public class HR {
public void processPaycheck(Employee employee) {
double salary = employee.getSalary();
// ... logic to process paycheck
System.out.println("Processing paycheck for amount: " + salary);
}
}

In this case, the HR class has a direct dependency on the Employee class. If the Employee class changes—for example, if the getSalary() method is renamed or its return type changes—the HR class will break. This is a simple example of a direct dependency.

A better approach is to depend on abstractions, not concrete implementations. For instance, testing classes should only rely on the public interfaces of the classes they test. This principle limits breakage when internal implementation details change, making the codebase more resilient and maintainable. For scope and technique, see unit vs functional testing and regression vs unit testing.

Conclusion

Dependencies are an integral part of modern software development. They enable us to build powerful applications by standing on the shoulders of giants. However, this power comes with responsibility. A failure to manage dependencies is a failure to manage your project's quality, security, and performance.

By understanding the different types of dependencies, from external libraries to internal modules, you can make more informed architectural decisions. Using the right tools and best practices—like package managers, virtual environments, and SCA scanners—transforms dependency management from a chore into a strategic advantage. It leads to better code, safer deployments, and smoother collaboration. The central question of what are dependencies in code is one every developer must answer to build professional-grade software.

FAQ Section

1) What are examples of dependencies?

Dependencies include software libraries (e.g., Lodash), external modules (npm packages), internal shared utilities, test frameworks (a build dependency), and runtime libraries like database connectors.

2) What do you mean by dependencies?

Dependencies are external or internal pieces of code that your project requires to function correctly. Your code "depends" on them to execute its tasks.

3) What are the dependencies of a programming language?

These include its runtime environment (like an interpreter or compiler), its standard library of built-in functions, and its toolchain, which consists of package managers and build tools.

4) What are dependencies on a computer?

These are system-level libraries or packages an application needs to run. Examples include graphics drivers, system fonts like OpenSSL, or installed runtimes such as the Java Virtual Machine (JVM) or .NET Framework.

Shivam Agarwal

Figma Design To Code: Step-by-Step Guide 2025

Why “Figma Design to Code” Matters

The benefits are immediate and measurable.

Saves Time: Research shows that development can be significantly faster with automated systems. A study by Sparkbox found that using a design system made a simple form page 47% faster to develop versus coding it from scratch. This frees up developers to focus on complex logic.
Reduces Errors: Manual translation introduces human error. Automated conversion ensures visual and structural consistency between the Figma file and the initial codebase. According to Aufait UX, teams using design systems can reduce errors by as much as 60%.
Smoother Collaboration: Tools that automate code generation act as a common language between designers and developers. They reduce the back-and-forth communication that often plagues projects. Studies on designer-developer collaboration frequently point to communication issues as a primary challenge.

This approach helps both non-coders and frontend developers. It provides a direct path to creating responsive layouts and functional components, accelerating the entire development lifecycle.

Getting Started with Dualite Alpha

Its core strengths are:

Direct Figma Integration: Dualite works with Figma without needing an extra plugin. You can connect your designs directly.
Automated Code Generation: The platform intelligently interprets Figma designs to produce clean, structured code.
Frontend Framework Support: It generates code for React, Tailwind CSS, and plain HTML/CSS, fitting into modern tech stacks.

Dualite serves as a powerful accelerator for any team looking to improve its Figma design to code workflow.

Figma Design to Code: Step-by-Step Tutorial

Step 1: Open Dualite and Connect Your Figma Account

Step 2: Copy the Link to Your Figma Selection

Step 3: Import Your Figma Design into Dualite

Step 4: Confirm and Continue

Review the preview to ensure it accurately represents your selection. If everything looks correct, click "Continue with this design" to proceed.

Step 5: Select the Target Stack and Generate the Initial Build

Step 6: Iterate and Refine with Chat Commands

Step 7: Inspect, Edit, and Export Your Code

Step 8: Deploy Your Website

Finally, to publish your site, click "Deploy" in the top-right corner and connect your Netlify account.

Conclusion

Dualite simplifies the Figma design to code process. It provides a practical, efficient solution for turning visual concepts into tangible frontend code.

FAQ Section

1) Can I convert Figma design to code?

Yes. Tools like Dualite let you convert Figma designs into React, HTML/CSS, or Tailwind CSS code with a few clicks. Figma alone provides only basic CSS snippets, not full layouts or structure.

2) Can ChatGPT convert Figma design to code?

Not directly. ChatGPT cannot parse Figma files. You can describe a design and ask for code suggestions, but it cannot generate accurate front-end layouts from actual Figma prototypes.

3) Does Figma provide code for design?

Figma’s Dev Mode offers CSS and SVG snippets, but not full production-ready code. Most developers still hand-write the structure, style, and logic based on those hints.

4) What tool converts Figma to code?

Dualite is one such tool that turns Figma designs into clean code quickly. Other tools exist, but users report mixed results—often fine for prototypes, but not always clean or maintainable.

Figma & No-code

Shivam Agarwal

Featured image for an article on Secure code review checklist

Secure Code Review Checklist for Developers

TL;DR: Secure Code Review Checklist

A secure code review checklist is a structured guide to ensure code is free from common security flaws before reaching production. The core items include:

Input Validation – Validate and sanitize all user input on the server side.
Output Encoding – Use context-aware encoding to prevent XSS.
Authentication & Authorization – Enforce server-side checks, hash & salt passwords, follow least privilege.
Error Handling & Logging – Avoid leaking sensitive info, log security-relevant events without secrets.
Data Encryption – Encrypt data at rest and in transit using strong standards (TLS 1.2+, AES-256).
Session Management – Secure tokens, timeouts, HttpOnly & Secure cookies.
Dependency Management – Use SCA tools, keep libraries updated.
Logging & Monitoring – Track suspicious activity, monitor alerts, protect log files.
Threat Modeling – Continuously validate assumptions and attack vectors.
Secure Coding Practices – Follow OWASP, CERT, and language-specific standards.

Use this checklist during manual reviews, supported by automation (SAST/SCA tools), to catch vulnerabilities early, reduce costs, and standardize secure development practices.

Why Use a Secure Code Review Checklist?

Standardization: Ensures every piece of code gets the same security scrutiny.
Efficiency: Guides reviewers to the most critical areas quickly.
Early Detection: Finds and fixes flaws before they become major problems.
Knowledge Sharing: Acts as a teaching tool for junior developers.

Preparing Your Secure Code Review

Threat Modeling First

Before reviewing code, you must understand the application's potential threats. Threat modeling is a process where you identify security risks and potential vulnerabilities.

Ask questions like:

Where does the application handle sensitive data?
What are the entry points for user input?
How do different components authenticate with each other?
What external systems does the application trust?

This analysis helps you pinpoint high-risk areas of the codebase architecture that demand the most attention.

Define Objectives

Set Scope

You do not have to review the entire codebase at once. Start with the most critical and high-risk code segments identified during threat modeling.

Focus initial efforts on:

Authentication and Authorization Logic: Code that handles user logins and permissions.
Session Management: Functions that create and manage user sessions.
Data Encryption Routines: Any code that encrypts or decrypts sensitive information.
Input Handling: Components that process data from users or external systems.

Gather the Right Tools and People

Core Secure Code Review Checklist Items

This section contains the fundamental items that should be part of any review. Each one targets a common area where security vulnerabilities appear.

1) Input Validation

Attackers exploit applications by sending malicious or unexpected input. Proper input validation is your first line of defense.

Validate on the Server Side: Never trust client-side validation alone. Attackers can easily bypass it. Always re-validate all inputs on the server.
Classify Data: Separate data into trusted (from internal systems) and untrusted (from users or external APIs) sources. Scrutinize all untrusted data.
Centralize Routines: Create and use a single, well-tested library for all input validation. This avoids duplicated effort and inconsistent logic.
Canonicalize Inputs: Convert all input into a standard, simplified form before processing. For example, enforce UTF-8 encoding to prevent encoding-based attacks.

2) Output Encoding

Output encoding prevents attackers from injecting malicious scripts into the content sent to a user's browser. This is the primary defense against Cross-Site Scripting (XSS).

Encode on the Server: Always perform output encoding on the server, just before sending it to the client.
Use Context-Aware Encoding: The method of encoding depends on where the data will be placed. Use specific routines for HTML bodies, HTML attributes, JavaScript, and CSS.
Utilize Safe Libraries: Employ well-tested libraries provided by your framework to handle encoding. Avoid writing your own encoding functions.

3) Authentication & Authorization

Authentication confirms a user's identity, while authorization determines what they are allowed to do. Flaws in these areas can give attackers complete control.

Enforce on the Server: All authentication and authorization checks must occur on the server.
Use Tested Services: Whenever possible, integrate with established identity providers or use your framework's built-in authentication mechanisms.
Centralize Logic: Place all authorization checks in a single, reusable location to ensure consistency.
Hash and Salt Passwords: Never store passwords in plain text. Use a strong, adaptive hashing algorithm like Argon2 or bcrypt with a unique salt for each user.
Use Vague Error Messages: On login pages, use generic messages like "Invalid username or password." Specific messages ("User not found") help attackers identify valid accounts.
Secure External Credentials: Protect API keys, database credentials, and other secrets. Store them outside of your codebase using a secrets management tool.

4) Error Handling & Logging

Proper error handling prevents your application from leaking sensitive information when something goes wrong.

Avoid Sensitive Data in Errors: Error messages shown to users should never contain stack traces, database queries, or other internal system details.
Log Sufficient Context: Your internal logs should contain enough information for debugging, such as a timestamp, the affected user ID (if applicable), and the error details.
Do Not Log Secrets: Ensure that passwords, API keys, session tokens, and other sensitive data are never written to logs.

5) Data Encryption

Data must be protected both when it is stored (at rest) and when it is being transmitted (in transit).

Encrypt Data in Transit: Use Transport Layer Security (TLS) 1.2 or higher for all communication between the client and server.
Encrypt Data at Rest: Protect sensitive data stored in databases, files, or backups.
Use Proven Standards: Implement strong, industry-accepted encryption algorithms like AES-256. For databases, use features like Transparent Data Encryption (TDE) or column-level encryption for the most sensitive fields.

6) Session Management & Access Controls

Once a user is authenticated, their session must be managed securely. Access controls ensure users can only perform actions they are authorized for.

Secure Session Tokens: Generate long, random, and unpredictable session identifiers. Do not include any sensitive information within the token itself.
Expire Sessions Properly: Sessions should time out after a reasonable period of inactivity. Provide users with a clear log-out function that invalidates the session on the server.
Guard Cookies: Set the Secure and HttpOnly flags on session cookies. This prevents them from being sent over unencrypted connections or accessed by client-side scripts.
Enforce Least Privilege: Users and system components should only have the minimum permissions necessary to perform their functions.

7) Dependency Management

Modern applications are built on a foundation of third-party libraries and frameworks. A vulnerability in one of these dependencies is a vulnerability in your application.

Use Software Composition Analysis (SCA) Tools: These tools scan your project to identify third-party components with known vulnerabilities.
Keep Dependencies Updated: Regularly update your dependencies to their latest stable versions. Studies from organizations like Snyk regularly show that a majority of open-source vulnerabilities have fixes available. A 2025 Snyk report showed projects using automated dependency checkers fix vulnerabilities 40% faster.

8) Logging & Monitoring

Secure logging and monitoring help you detect and respond to attacks in real-time.

Track Suspicious Activity: Log security-sensitive events such as failed login attempts, access-denied errors, and changes to permissions.
Monitor Logs: Use automated tools to monitor logs for patterns that could indicate an attack. Set up alerts for high-priority events.
Protect Your Logs: Ensure that log files are protected from unauthorized access or modification.

9) Threat Modeling

During the review, continuously refer back to your threat model. This helps maintain focus on the most likely attack vectors.

Review Data Flows: Trace how data moves through the application.
Validate Trust Boundaries: Pay close attention to points where the application interacts with external systems or receives user input.
Question Assumptions: Could an attacker manipulate this data flow? Could they inject code or bypass a security control?

10) Code Readability & Secure Coding Standards

Clean, readable code is easier to secure. Ambiguous or overly complex logic can hide subtle security flaws.

Write Clear Code: Use meaningful variable names, add comments where necessary, and keep functions short and focused.
Use Coding Standards: Adhere to established secure coding standards for your language. Some great resources are the OWASP Secure Coding Practices, the SEI CERT Coding Standards, and language-specific guides.

11) Secure Data Storage

How and where you store sensitive data is critical. This goes beyond just encrypting the database.

Protect Backups: Ensure that database backups are encrypted and stored in a secure location with restricted access.
Sanitize Data: When using production data in testing or development environments, make sure to sanitize it to remove any real user information.
Limit Data Retention: Only store sensitive data for as long as it is absolutely necessary. Implement and follow a clear data retention policy.

Automated Tools to Boost Your Checklist

Integrate SAST and SCA into CI/CD

SAST Tools: These tools analyze your source code without executing it. They are excellent at finding vulnerabilities like SQL injection, buffer overflows, and insecure configurations.
SCA Tools: These tools identify all the open-source libraries in your codebase and check them against a database of known vulnerabilities.

Configure Security-Focused Rules

Popular Static Analysis Tools

Several tools can help automate parts of your review:

PVS-Studio: A static analyzer for C, C++, C#, and Java code.
Semgrep: A fast, open-source static analysis tool that supports many languages and allows for custom rules.
SonarQube: An open-platform to manage code quality, which includes security analysis features.

Running The Review

With your preparation complete and checklist in hand, it is time to conduct the review. A structured approach makes the process more efficient and less draining for the participants.

Timebox Your Sessions

Apply the Checklist Systematically

Work through your checklist steadily. Start with the high-risk areas you identified during threat modeling. Use a combination of automated tools and manual inspection.

Run Automated Scans First: Let SAST and SCA tools perform an initial pass to catch low-hanging fruit.
Manually Inspect High-Risk Code: Use your expertise and the checklist to examine authentication, authorization, and data handling logic.
Validate Business Logic: Check for flaws in the application's logic that an automated tool would miss.

Track Metrics for Improvement

To make your process repeatable and measurable, track key metrics.

Metric	Description	Purpose	Tracking Tools
Inspection Rate	Lines of code reviewed per hour.	Helps in planning future reviews.	Code review systems (Crucible, Gerrit) or custom dashboards (Grafana, Tableau) pulling data from version control.
Defect Density	Number of defects found per 1,000 lines of code.	Measures code quality over time.	Static analysis tools (SonarQube) and issue trackers (Jira, GitHub Issues).
Time to Remediate	Time taken to fix a reported issue.	Measures the efficiency of your response process.	Issue trackers like Jira, GitHub Issues, Asana, or service desk software like Zendesk.

Keeping Your Process Up to Date

Security is not a one-time activity. The threat environment is constantly changing, and your review process must adapt. An effective secure code review checklist is a living document.

Update for New Threats

Build a Security-First Mindset

Sample “Starter” Checklist

For a more detailed baseline, the OWASP Code Review Guide and the associated Quick Reference Guide are excellent resources.

Input Validation

[Critical] Is the application protected against injection attacks (SQLi, XSS, Command Injection)?
[Critical] Is all untrusted input validated on the server side?
[High] Is input checked for length, type, and format?
[Medium] Is a centralized input validation routine used?

Authentication & Authorization

[Critical] Are all sensitive endpoints protected with server-side authentication checks?
[Critical] Are passwords hashed using a strong, salted algorithm (e.g., Argon2, bcrypt)?
[Critical] Are authorization checks performed based on the user's role and permissions, not on incoming parameters?
[High] Are account lockout mechanisms in place to prevent brute-force attacks?
[High] Does the principle of least privilege apply to all user roles?

Session Management

[Critical] Are session tokens generated with a cryptographically secure random number generator?
[High] Are session cookies configured with the HttpOnly and Secure flags?
[High] Is there a secure log-out function that invalidates the session on the server?
[Medium] Do sessions time out after a reasonable period of inactivity?

Data Handling & Encryption

[Critical] Is all sensitive data encrypted in transit using TLS 1.2+?
[High] Is sensitive data encrypted at rest in the database and in backups?
[High] Are industry-standard encryption algorithms (e.g., AES-256) used?
[Medium] Are sensitive data or system details avoided in error messages?

Dependency Management

[High] Has an SCA tool been run to check for vulnerable third-party libraries?
[High] Are all dependencies up to their latest secure versions?

Logging & Monitoring

[Critical] Are secrets (passwords, API keys) excluded from all logs?
[Medium] Are security-relevant events (e.g., failed logins, access denials) logged?

Conclusion

FAQs

1) What are the 7 steps to review code?

A standard secure code review process involves seven steps:

Define review goals and scope.
Gather the code and related artifacts.
Run automated SAST/SCA tools for an initial scan.
Perform a manual review using a checklist, focusing on high-risk areas.
Document all findings clearly with actionable steps.
Prioritize the documented issues based on risk.
Remediate the issues and verify the fixes.

2) How to perform a secure code review?

3) What is a code review checklist?

4) What are SAST tools during code review?

5) How long should a secure code review take per 1,000 LOC?

There isn't a strict time rule, as the duration depends on several factors. However, a general industry guideline for a manual review is between 1 to 4 hours per 1,000 lines of code (LOC).

Factors that influence this timing include:

Code Complexity: Complex business logic or convoluted code will take longer to analyze than simple, straightforward code.
Reviewer's Experience: A seasoned security professional will often be faster and more effective than someone new to code review.
Programming Language: Some languages and frameworks have more inherent security risks and require more scrutiny.
Scope and Depth: A quick check for the OWASP Top 10 vulnerabilities is much faster than a deep, architectural security review.

LLM & Gen AI

Shivam Agarwal

Code Dependencies: What They Are and Why They Matter

What “Dependency” Really Means in Programming

In software development, it's useful to distinguish between the general concept of dependence and the concrete term dependency.

Dependence is the state of relying on an external component for your code to function. It describes the "need" itself.
A dependency is the actual component you are relying on, such as a specific library, package, or framework.

Libraries, External Modules, and Internal Code

It's useful to differentiate between a few common terms:

Software Libraries: These are collections of pre-written code that developers can use. For example, a library like NumPy in Python might offer functions for complex mathematical calculations. You import the library and call its functions.
External Modules: This is a similar concept. An external module is a self-contained unit of code that exists outside your primary project codebase. Package managers install these modules for you to use. A well-known example is React, which is used for building user interfaces.
Internal Modular Code: These are dependencies within your own project. You might break your application into smaller, reusable modules. For instance, a userAuth.js module could be used by both the authentication and profile sections of your application, creating an internal dependency.

A Community Perspective

Types of Code Dependencies: An Organized Look

Common Dependency Categories

Here is a look at the most common types of dependencies you will work with.

Library Dependencies: These are the most common type. They consist of third-party code you import to perform specific tasks. Examples include react for building user interfaces or pandas for data manipulation in Python.
External Modules: This is a broad term for any code outside your immediate project. It includes libraries, frameworks, and any other packages you pull into your tech stack from an external registry.
Internal (Modular) Dependencies: These exist inside your project's codebase. When you structure your application into distinct modules, one module might require another to function. This creates a dependency between internal parts of your code.
Build Dependencies: These are tools required to build or compile your project. They are not needed for the final application to run, but they are essential during the development and compilation phase. A code transpiler like Babel is a classic example.
Compile-time Dependencies: These are similar to build dependencies. They are necessary only when the code is being compiled. For example, a C++ project might depend on header files that are not needed once the executable is created.
Runtime Dependencies: These are required when the application is actually running. A database connector, for instance, is a runtime dependency. The application needs it to connect to the database and execute queries in the production environment.

Transitive Dependencies

Practical Example

Imagine you're building a web application using Node.js:

Direct Dependency: You add a library called Auth-Master to your project to handle user logins. Auth-Master is a direct dependency.
Transitive Dependency: Auth-Master requires another small utility library, Token-Gen, to create secure session tokens. You didn't add Token-Gen yourself, but your project now depends on it transitively.
Runtime Dependency: For the application to function at all, it must be executed by the Node.js runtime environment. Node.js is a runtime dependency. In this case, both Auth-Master and Token-Gen are also runtime dependencies because they are needed when the application is running to manage logins.

Why Code Dependencies Matter (and Why You Should Care)

1. Code Quality & Maintenance

2. Security

3. Performance

4. Legal & Licensing

Managing Code Dependencies Like a Pro

a. Dependency Management Tools

npm (Node.js): The default package manager for JavaScript. It manages packages listed in a package.json file.
pip (Python): Used to install and manage Python packages. It typically works with a requirements.txt file.
Maven / Gradle (Java): These are build automation tools that also handle dependency management for Java projects.
Yarn / pnpm: Alternatives to npm that offer improvements in performance and security for managing JavaScript packages.

These tools streamline the installation process and help resolve version conflicts between different libraries.

b. Virtual Environments

c. Semantic Versioning

Semantic Versioning (SemVer) is a versioning standard that provides meaning to version numbers. A version is specified as MAJOR.MINOR.PATCH.

MAJOR version change indicates an incompatible API change.
MINOR version change adds functionality in a backward-compatible manner.
PATCH version change makes backward-compatible bug fixes.

d. Visualization & Analysis Tools

For complex projects, it can be difficult to see the full dependency tree. This is where visualization and analysis tools come in.

Software Composition Analysis (SCA) Tools: These tools scan your project to identify all open-source components, including transitive dependencies. They check for known security vulnerabilities and potential license conflicts. The OWASP Dependency-Check project is a well-known open-source SCA tool.
Dependency Graph Visualizers: Tools like CodeSee's dependency maps can generate interactive diagrams of your codebase. These visualizations help you understand how modules interact and identify areas of high complexity or tight coupling.

e. Refactoring for Modularity

Real-World Example in OOP

Imagine two classes in an HR system: Employee and HR.

Conclusion

FAQ Section

1) What are examples of dependencies?

2) What do you mean by dependencies?

Dependencies are external or internal pieces of code that your project requires to function correctly. Your code "depends" on them to execute its tasks.

3) What are the dependencies of a programming language?

These include its runtime environment (like an interpreter or compiler), its standard library of built-in functions, and its toolchain, which consists of package managers and build tools.

4) What are dependencies on a computer?

Shivam Agarwal

Featured image for an article on Visual scripting

Visual Scripting: Definition, Benefits, and Examples

Imagine building application logic like assembling a flowchart. You connect boxes and arrows on a screen, defining behavior and flow without writing a single line of traditional code. This node-based, drag-and-drop approach is the foundation of a powerful method that is changing how teams build interactive experiences. This brings us to the core question: what is visual scripting?

For developers, tech leads, and engineering teams, understanding this approach is vital. It offers a way to accelerate prototyping, improve collaboration between technical and non-technical staff, and automate workflows. It represents a significant shift in how we can structure and visualize computational logic, making it an essential tool in modern development, from game creation to interactive design.

What Is Visual Scripting?

At its heart, visual scripting is a method of programming that lets you construct application logic using a graphical interface instead of text-based code. Users manipulate graphical elements—called nodes or blocks—and connect them to create a flow of actions and decisions.

Each node represents a specific function, event, variable, or control flow statement. For example, one node might get a character’s position, another might check for user input, and a third could trigger an animation. You connect these nodes with wires or lines, dictating the sequence and logic of operations in a clear, visual manner.

This method provides an abstraction layer over conventional programming. It allows creators to focus on the logic and behavior of their application without getting bogged down by the syntax of a specific programming language. It is a practical answer to what is visual scripting.

How Visual Scripting Works

The mechanics of visual scripting are straightforward and intuitive. The process typically involves a few simple steps. You start by dragging nodes or blocks from a library onto a canvas. Then, you connect these nodes to map out the logical flow of your program.

Nodes: These are the basic building blocks. They can represent anything from a mathematical operation (add, subtract) to a complex action (play sound, move object).
Wires: These are the connectors that establish relationships between nodes. They direct the flow of data and execution from one node to the next.
Graphs: The entire canvas of connected nodes is called a graph. This graph is a visual representation of a script or a piece of your codebase architecture.

Behind the scenes, this visual graph is translated into machine-readable code. This translation layer converts the node-based logic into a language that the underlying engine can execute, such as C++ or C#. This means you are still programming, just through a different interface.

Many popular game engines and development toolchains feature robust visual scripting systems.

Unreal Engine’s Blueprints is a premier example, deeply integrated into the engine and best for game devs building complex interactions visually.
Unity’s Visual Scripting (formerly known as Bolt) offers similar functionality and is best for teams mixing coders and non-coders; it was made a free, standard part of the engine in 2020.

These tools demonstrate how visual systems can coexist with and complement traditional code within a professional tech stack.

Why It Works: The Benefits for Engineering Teams

Understanding the advantages helps clarify the utility of visual scripting. It introduces efficiency and accessibility into the development process. The benefits directly address common production bottlenecks.

Accessible Interface: The graphical approach lowers the barrier to entry. Designers, artists, and other non-programmers can quickly contribute to the project’s logic without needing to learn complex syntax. This makes it a powerful tool for teams with varied technical skills.
Speed & Prototyping: Visual scripting excels at rapid iteration. You can build and test ideas, create proof-of-concepts, and produce functional demos much faster than with traditional coding. This speed is invaluable for validating concepts in early development stages.
Reduced Syntax Errors & Complexity: Because you work with pre-defined nodes, typographical and syntactical mistakes are nearly eliminated. This allows you to concentrate on the logic itself rather than debugging missing semicolons or mismatched brackets. The visual flow simplifies the representation of program logic.
Better Collaboration: This method acts as a common language between developers and non-technical team members. A designer can create a UI flow visually, and a programmer can then inspect the underlying graph or even convert it to code for optimization. This shared workspace improves communication and integration.
Code Scaffold & Boilerplate: Visual tools can scaffold logical structures very quickly. You can generate the basic architecture for a system visually and then transition to text-based code to refine performance-critical parts. This saves time writing repetitive boilerplate code.

Drawbacks and Limitations to Consider

Despite its benefits, visual scripting is not a universal solution. Engineering teams must be aware of its limitations to apply it effectively and avoid creating future technical debt.

Scalability & Maintenance Issues: As logic becomes more complex, visual graphs can turn into a tangled web of nodes and wires, often called a "spaghetti graph." These large, intricate graphs are difficult to debug, refactor, and maintain over the long term. Reading and modifying a massive visual script is often less efficient than working with well-structured text code.
Performance Concerns: Visual scripting often introduces a small performance overhead compared to handwritten code. For most tasks, this difference is negligible. But for performance-critical systems—like core gameplay mechanics or high-frequency data processing—this overhead can become a significant issue.
Refactoring Constraints: Automated refactoring tools for visual scripts are less mature than those for text-based languages. Restructuring or cleaning up a complex visual graph is largely a manual process, which can be time-consuming and prone to error.
Ideal Use Cases Only: It is best seen as an ancillary tool within a larger development toolset. It is perfect for certain tasks, such as UI logic, state machines, or simple event handling. However, it is not the right choice for building the entire backbone of a complex software system.

Visual Scripting: Real-World Developer Perspectives

To ground this discussion in practical experience, consider what developers actively working in the field have to say. Conversations on platforms like Reddit offer candid insights into how teams integrate these tools.

One developer highlights its value for initial builds but points out the need to transition later:

“We use it during the prototyping phase... we generally tend to remove most of the visual scripting during production to allow for more optimization and refactoring options in the long run.”

This sentiment is common. The tool helps teams validate ideas quickly before committing to a production-ready codebase.

Another developer offers a warning on growing complexity:

“Visual scripting becomes a big problem when the scope gets larger – reading through code is much easier than trying to scroll around to see which wire is going where.”

This quote speaks directly to the scalability and maintenance challenges mentioned earlier.

A balanced view treats it as a specialized instrument:

“Visual scripting is the microwave oven of the gamedev toolset... excel in very specific situations and require a fair bit of knowledge on how to actually use them correctly.”

This analogy correctly positions it as one tool among many, not a complete replacement for a traditional kitchen.

Finally, a developer points to its strength in empowering designers to make content adjustments:

“It is faster to implement... your system becomes highly extendable... can be used by the designer.”

This ability for non-programmers to iterate on logic is a significant production benefit. These perspectives help answer the question of what is visual scripting in a practical context.

Examples and Use Cases

The application of visual scripting extends across various domains, with game development being the most prominent. Leading engines provide first-class support for this workflow.

Unity (Visual Scripting): Since Unity acquired Bolt in 2020 and integrated it as a free package, its visual scripting tool has become a core part of the ecosystem. It allows teams to create logic for everything from character controllers to UI management directly within the editor. The question of what is visual scripting is often answered by pointing to Unity's implementation.
Unreal Engine (Blueprints): Blueprints are arguably the most famous visual scripting system. They are deeply integrated into Unreal Engine and are used by indie developers and AAA studios alike. Many full games have been shipped using Blueprints for a substantial portion of their codebase.
Workflow Automation & Interactive Design: The usefulness of node-based logic is not limited to games. It is found in tools for creating interactive installations, automating software tasks, and customizing application behavior. This approach lets users visually configure complex workflows without writing code.
Low-Code Testing: An adjacent field is low-code testing automation. Tools in this area often use drag-and-drop interfaces to build test scripts, allowing quality assurance teams to create and manage automated tests visually. This is another example of what is visual scripting enabling non-programmers.

Tips for Developers and Tech Leads

To integrate visual scripting effectively into your workflow, you should follow a few best practices. This ensures you get the benefits without falling into common pitfalls.

Use it for Prototyping and High-Level Logic: It is ideal for quickly testing game mechanics, setting up state machines, or defining UI flows. Use it when non-coders need to contribute to the logic.
Avoid Over-Reliance: For systems that require high performance or are algorithmically complex, transition to traditional, text-based code. Use the visual script as a scaffold, then rewrite critical parts in C# or C++.
Keep Graphs Small and Modular: Just as you would write short, single-responsibility functions in code, you should create small, focused visual graphs. Use subgraphs to encapsulate and reuse logic, preventing your main graphs from becoming unmanageable.
Tackle Complex Problems with Community Knowledge: When faced with a tricky bug or a complex implementation in either visual scripts or traditional code, it is common to get stuck. Before modern automated tools became prevalent, developers relied heavily on community-driven platforms. Websites like Stack Overflow, engine-specific forums, and official documentation are invaluable resources. Searching for similar problems or posting a well-defined question can provide solutions and insights from experienced peers, helping you overcome hurdles without reinventing the wheel.
Establish Clear Conventions: Your team should agree on standards for naming, layout, and commenting within visual graphs. This discipline is crucial for keeping your visual codebase clean and maintainable. This approach helps in understanding what is visual scripting at a team-wide scale.

Conclusion

Visual scripting is an approachable, visual layer that sits on top of programming logic. It demystifies the process of creating behavior in software, making it accessible to a wider range of creators. Its strengths in rapid prototyping, team collaboration, and design-centered development are clear. For many, this is the complete answer to what is visual scripting.

However, it is not a replacement for text-based coding. According to Gartner, the market for low-code technologies is expanding rapidly, showing its importance. The best results come when it is used judiciously as part of a complete toolset, complementing traditional code rather than supplanting it.

The true value of visual scripting is proven through application, not theory. Challenge your team to build its next prototype using Unity Visual Scripting or Unreal Blueprints. The immediate improvement in development speed and workflow will speak for itself.

FAQ Section

1. What is visual scripting used for?

It is used to generate game logic, UI flows, interactive scenes, and prototypes. It is also applied in automation tools and workflow setups. It is particularly useful when you want to build logic visually or involve non-coders in the development process. This is the practical side of what is visual scripting.

2. Is visual scripting easier than coding?

It is often easier for simple logic because it hides syntax and lets you connect concepts visually. However, for complex or large-scale systems, traditional coding provides more control, clarity, and better tools for maintenance and refactoring.

3. Can you make a game with visual scripting?

Absolutely. Many prototypes and indie projects are built entirely with tools like Unity Visual Scripting or Unreal Blueprints. That said, most complex, commercially released games use a combination of visual scripting and traditional code to achieve their performance and scalability goals.

4. Was Hollow Knight made with visual scripting?

No available evidence suggests that Hollow Knight used a visual scripting system. It was built in Unity using traditional coding techniques. The game is a great example of what can be accomplished with a powerful engine and a well-structured C# codebase.

Figma & No-code

Shivam Agarwal