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TDD vs BDD: TDD ensures code correctness with tests-first approach. BDD focuses on user behavior alignment. Both enhance software development efficiency 30 November 2023 13 Min. Read TDD vs BDD: Key Differences WhatsApp LinkedIn X (Twitter) Copy link Get the full comparison sheet Software development has evolved significantly over the years, with methodologies focusing on enhancing efficiency and reliability. Two notable approaches in this evolution are Test-Driven Development (TDD) and Behavior-Driven Development (BDD) . Both aim to streamline the development process but differ in philosophy and execution. In this article, we'll explore both of these approaches in detail and examine how adopting either of them can benefit any development cycle. What is TDD? In the fast-evolving landscape of software engineering, maintaining high code quality is paramount. Test-Driven Development (TDD) is not just a testing approach; it's a philosophy that encourages simplicity, clarity, and continuous improvement in software design. At its core, TDD is a software development approach where tests are written before the actual code. It operates on a simple cycle: 👉Write a failing test, 👉write the minimum code to pass the test, and 👉refactor the code for better design. TDD leads to a cleaner, more maintainable codebase. It encourages developers to think through requirements or design before writing the functional code, resulting in fewer bugs and more robust software solutions. TDD Workflow Red-Green-Refactor Cycle: This cycle starts with writing a test that fails (Red), then writing code that makes the test pass (Green), and finally refactoring to improve the code's structure (Refactor). Imagine constructing a building: initially, you create a blueprint (the test), then you build according to the blueprint (write code), and finally, you enhance and beautify your construction (refactor). # Python Example def test_addition(): assert addition(2, 3) == 5 def addition(a, b): return a + b How to Perform TDD? Test-Driven Development (TDD) is a software development process where tests are written before the actual code. The process typically follows a cycle known as "Red-Green-Refactor". Here's a step-by-step guide, along with an example using a simple function in Python: Step 1: Understand the Requirement Before writing any test, you must have a clear understanding of what the function or module is supposed to do. For example, let's consider a requirement for a function called add that takes two numbers and returns their sum. Step 2: Write a Failing Test (Red Phase) You begin by writing a test for the functionality that doesn't exist yet. This test will fail initially (hence the "Red" phase). Example: def test_add(): assert add(2, 3) == 5 This test will fail because the add function doesn't exist yet. Step 3: Write the Minimum Code to Pass the Test (Green Phase) Now, write the simplest code to make the test pass. Example: def add(x, y): return x + y With this code, the test should pass, bringing us to the "Green" phase. Step 4: Refactor the Code After the test passes, you can refactor the code. This step is about cleaning up the code without changing its functionality. Example of Refactoring: def add(x, y): # Refactoring to make the code cleaner return sum([x, y]) Step 5: Repeat the Cycle For adding more functionality or handling different cases, go back to Step 2. Write a new test that fails, then write code to pass the test, and finally refactor. Example: Extending the add Function Let's say you want to extend the add function to handle more than two numbers. New Test (Red Phase) def test_add_multiple_numbers(): assert add(2, 3, 4) == 9 Update Code to Pass (Green Phase) def add(*args): return sum(args) Refactor (if needed) Refactor the code if there are any improvements to be made. Best Practices To Implement TDD Implementing Test-Driven Development (TDD) effectively requires adherence to a set of good practices. Follow through this list to get an idea: Start with Simple Tests: Begin by writing simple tests for the smallest possible functionality. This helps in focusing on one aspect of the implementation at a time. def test_add_two_numbers(): assert add(1, 2) == 3 Test for Failures Early: Write tests that are expected to fail initially. This ensures that your test suite is correctly detecting errors and that your implementation later satisfies the test. def test_divide_by_zero(): with pytest.raises(ZeroDivisionError): divide(10, 0) Minimal Implementation: Write the minimum amount of code required to pass the current set of tests. This encourages simplicity and efficiency in code. Refactor Regularly: After passing the tests, refactor your code to improve readability, performance, and maintainability. Refactoring should not alter the behavior of the code. One Logical Assertion per Test: Each test case should ideally have one logical assertion. This makes it clear what aspect of the code is being tested and helps in identifying failures quickly. Test Behaviors, Not Methods: Focus on testing the behavior of the code rather than its internal implementation. This means writing tests for how the system should behave under certain conditions. Continuous Integration: Integrate your code frequently and run tests to catch integration issues early. Avoid Testing External Dependencies: Don't write TDD tests for external libraries or frameworks. Instead, use mocks or stubs to simulate their behavior. Readable Test Names: Name your tests descriptively. This acts as documentation and helps in understanding the purpose of the test. def test_sorting_empty_list_returns_empty_list(): assert sort([]) == [] Keep Tests Independent: Ensure that each test is independent of others. Tests should not rely on shared state or the result of another test. Common Challenges in Implementing TDD Approach Implementing Test-Driven Development (TDD) can be a powerful approach to software development, but it comes with its own set of challenges. Here are some common obstacles encountered while adopting TDD, along with examples: Cultural Shift in Development Teams: TDD requires a significant mindset change from traditional development practices. Developers are accustomed to writing code first and then testing it. TDD flips this by requiring tests to be written before the actual code. This can be a hard adjustment for some teams. Learning Curve and Training: TDD demands a good understanding of writing effective tests. Developers who are new to TDD might struggle with what constitutes a good test and how to write tests that cover all scenarios. Integration with Existing Codebases: Applying TDD to a new project is one thing, but integrating it into an existing, non-TDD codebase is a significant challenge. This might involve rewriting significant portions of the code to make it testable. A large legacy system, for example, might have tightly coupled components that are hard to test individually. Balancing Over-testing and Under-testing: Finding the right level of testing is crucial in TDD. Over-testing can lead to wasted effort and time, whereas under-testing can miss critical bugs. Maintaining Test Suites: As the codebase grows, so does the test suite. Maintaining this suite, ensuring tests are up-to-date, and that they cover new features and changes can be challenging. Complexity in Test Cases: As applications become more complex, so do their test cases. Writing effective tests for complex scenarios, like testing asynchronous code or handling external dependencies, can be challenging and sometimes lead to flaky tests. Adopting TDD is not just about technical changes but also involves cultural and process shifts within a team or an organization. While the challenges are significant, the long-term benefits of higher code quality, better design, and reduced bug rates often justify the initial investment in adopting TDD. Benefits of TDD Approach Better Quality Software: Repeated refactoring results in enhanced code quality and adherence to requirements. Faster Development: TDD can significantly reduce bug density, thereby reducing the time and cost of development in the long run. Ease of Maintenance: The codebase becomes more maintainable due to fewer bugs. Project Cost Efficiency: It reduces the costs associated with fixing bugs at later stages. Increased Developer Motivation: The successful passing of tests instills confidence and motivation in developers. Learn how adopting TDD approach led to a better product quality, faster releases, and higher customer and developer satisfaction for TechFlow Inc, which is a medium-sized development company. What is BDD? BDD is a software development process that focuses on the system's behavior as perceived by the end user. It emphasizes collaboration among developers, testers, and stakeholders. Development begins by defining the expected behavior of the system, often described in a simple and understandable language, which is then translated into code. Behavior-Driven Development (BDD) starts with clear, user-centric scenarios written in simple language , allowing for a shared understanding among developers, QA, and non-technical team members. 👉These scenarios are then converted into automated tests, guiding development to ensure the final product aligns with business goals and user needs. 👉BDD bridges communication gaps, encourages continuous collaboration, and creates living documentation that evolves with the project. BDD Workflow In BDD, scenarios are written in a human-readable format, usually following a " Given-When-Then " structure. These scenarios describe how the software should behave in various situations. 👉Define behavior in human-readable sentences. 👉Write scenarios to meet the behavior. 👉Implement code to pass scenarios. How to Perform BDD? Behavior-Driven Development (BDD) is an extension of Test-Driven Development (TDD) that focuses on the behavioral specification of software units. The key difference between TDD and BDD is that BDD tests are written in a language that non-programmers can read, making it easier to involve stakeholders in understanding and developing the specifications. Here's a step-by-step guide on how to perform BDD, with an example using Python and a popular BDD framework, Behave. Step 1: Define Feature and Scenarios BDD starts with writing user stories and scenarios in a language that is understandable to all stakeholders. These are typically written in Gherkin language, which uses a simple, domain-specific language. Example Feature File (addition.feature): Feature: Addition In order to avoid silly mistakes As a math idiot I want to be told the sum of two numbers Scenario: Add two numbers Given I have entered 50 into the calculator And I have entered 70 into the calculator When I press add Then the result should be 120 on the screen Step 2: Implement Step Definitions Based on the scenarios defined in the feature file, you write step definitions. These are the actual tests that run against your code. Example Step Definition (Python with Behave): from behave import * @given('I have entered {number} into the calculator') def step_impl(context, number): context.calculator.enter_number(int(number)) @when('I press add') def step_impl(context): context.result = context.calculator.add() @then('the result should be {number} on the screen') def step_impl(context, number): assert context.result == int(number) Step 3: Implement the Functionality Now, you implement the actual functionality to make the test pass. This is similar to the Green phase in TDD. Example Implementation ( calculator.py ): class Calculator: def __init__(self): self.numbers = [] def enter_number(self, number): self.numbers.append(number) def add(self): return sum(self.numbers) Step 4: Execute the Tests Run the BDD tests using the Behave command. The framework will match the steps in the feature file with the step definitions in your Python code and execute the tests. Step 5: Refactor and Repeat After the tests pass, you can refactor the code as needed. Then, for additional features, you repeat the process from Step 1. Best Practices To Implement BDD Define Behavior with User Stories: Start by writing user stories that clearly define the expected behavior of the application. Each story should focus on a specific feature from the user's perspective. Write Acceptance Criteria: For each user story, define clear acceptance criteria. These criteria should be specific, measurable, and testable conditions that the software must meet to be considered complete. Example: Given I am on the product page When I click 'Add to Cart' Then the item should be added to my shopping cart Use Domain-Specific Language (DSL): Utilize a DSL, like Gherkin, for writing your behavior specifications. This makes the behavior descriptions readable and understandable by all stakeholders, including non-technical team members. Feature: Shopping Cart Scenario: Add item to cart Given I am on the product page When I click 'Add to Cart' Then the item should be added to my shopping cart Automate Acceptance Tests: Translate your acceptance criteria into automated tests. These tests should guide your development process. Given(/^I am on the product page$/) do visit '/products' end When(/^I click 'Add to Cart'$/) do click_button 'Add to Cart' end Then(/^the item should be added to my shopping cart$/) do expect(page).to have_content 'Item added to cart' end Iterative Development: Implement features in small iterations, ensuring each iteration delivers a tangible, working product increment based on the user stories. Refactor Regularly: After the tests pass, refactor your code to improve clarity, remove redundancy, and enhance performance, ensuring the behavior remains unchanged. Encourage Collaboration: BDD is a collaborative process. Encourage regular discussions among developers, testers, and business stakeholders to ensure a shared understanding of the software behavior. Focus on User Experience: Prioritize the user experience in your tests. BDD is not just about functionality, but how the user interacts with and experiences the system. Documenting Behavior: Use the behavior descriptions as a form of documentation. They should be kept up-to-date as the source of truth for system functionality. Avoid Over-Specification: Write specifications that cover the intended behavior but avoid dictating the implementation details. This allows developers the flexibility to find the best implementation approach. Common Challenges in Implementing BDD Approach While BDD offers many benefits, it also presents several challenges, especially when being implemented for the first time. Here are some of the common challenges associated with BDD: Understanding and Implementing the BDD Process: BDD is more than a technical practice; it's a shift in how teams approach development. One common challenge is ensuring that all team members, not just developers, understand and effectively implement BDD principles. For instance , non-technical team members might struggle with the concept of writing behavior specifications in a structured format like Gherkin. Effective Collaboration Between Roles: BDD heavily relies on collaboration between developers, testers, and business stakeholders. Often, these groups have different backgrounds and expertise, which can lead to communication gaps. Writing Good Behavior Specifications: Writing effective and clear behavior specifications (like user stories) is a skill that needs to be developed. Poorly written specifications can lead to ambiguity and misinterpretation. Integrating BDD with Existing Processes: Introducing BDD into an existing development process can be challenging. It often requires changes in workflows, tools , and possibly even team structure. Training and Skill Development: BDD requires team members to develop new skills, including writing behavior specifications and automating tests. Balancing Detail in Specifications: Finding the right level of detail in behavior specifications is crucial. Too much detail can lead to rigid and brittle tests, while too little detail can result in tests that don’t adequately cover the intended behavior. Striking this balance is often a matter of trial and error. Benefits of BDD Approach Wider Involvement: BDD fosters collaboration among various team members, including clients. Clear Objectives: The use of simple language makes objectives clear to all team members. Better Feedback Loops: The involvement of more stakeholders leads to comprehensive feedback. Cost Efficiency: Like TDD, BDD also reduces the likelihood of late-stage bugs. Team Confidence: Clarity in requirements boosts team confidence and efficiency. Ease of Automation/Testing: Documentation in BDD is more accessible for automation testers. Applicability to Existing Systems: BDD tests can be implemented at any stage of development. Learn how adopting TDD approach led to a better product quality, faster releases, and higher customer and developer satisfaction for InnovateX, which is a medium-sized development company. TDD vs BDD: What to choose? The choice between TDD and BDD depends on various factors, including the project’s scope, team familiarity, and whether the system already exists. Both techniques have their place in software development and can be used together for optimal results in larger projects. Here’s a summarized version of comparison between TDD vs BDD to help you get started with what works for you the best. Conclusion In conclusion, TDD and BDD are powerful methodologies in the realm of software development. While they have their distinct features and benefits, they share the common goal of enhancing software quality and efficiency. The choice between them depends on the specific needs and context of a project. Understanding their nuances is essential for software teams to leverage their strengths effectively. Both methodologies aim to produce reliable, well-tested software, but they approach the problem from different angles and are suited to different environments and requirements. Still unsure of which approach to adopt between tdd vs bdd? Click here to learn how these approaches helped companies like InnovateX and TechFlow Inc to accelerate their bug-free release cycles. Related to Integration Testing Frequently Asked Questions 1. What is the main difference between BDD and TDD? Behavior-Driven Development (BDD) focuses on describing the system's behavior from a user's perspective using natural language, promoting collaboration between developers and non-technical stakeholders. Test-Driven Development (TDD) emphasizes writing tests before code to ensure functionality, aiding in design and refactoring. While both enhance software quality, BDD emphasizes collaboration and user-centric language, whereas TDD centers on code-centric testing. 2. What is an example of BDD? In BDD, a scenario might be: "Given a user is logged in, when they click 'Purchase,' then the item should be added to their cart." This user-centric, natural language scenario exemplifies BDD's focus on behavior. 3. What are the key concepts of BDD? Key concepts of BDD include writing scenarios in natural language to describe system behavior, using Given-When-Then syntax for clarity, fostering collaboration between developers and non-technical stakeholders, and emphasizing executable specifications. BDD aligns development with user expectations, promoting a shared understanding of desired outcomes. For your next read Dive deeper with these related posts! 09 Min. Read What is BDD (Behavior-Driven Development)? Learn More 10 Min. Read What is a CI/CD pipeline? Learn More 13 Min. Read TDD vs BDD: Key Differences Learn More

Discover the top contract testing tools for 2024. Streamline software integration with our recommended tools, ensuring reliability and compatibility in your development projects. 28 December 2023 09 Min. Read Top Contract Testing Tools Every Developer Should Know in 2024 Implement Contract Testing for Free WhatsApp LinkedIn X (Twitter) Copy link Microservices architecture has taken center stage in today's time, ensuring seamless communication between services is a critical imperative. Contract testing, a strategic approach to verifying the compatibility between services by defining their expected interactions, has emerged as a vital tool in the developer's arsenal. By identifying integration issues early in the development cycle, contract testing helps prevent costly downstream failures and ensures the overall stability and reliability of your applications. With a plethora of contract testing tools available, choosing the right one can be difficult. This blog post aims to simplify your decision-making process by highlighting the top contract testing tools that every developer should consider in 2024. List of Top Contract Testing Tools HyperTest PACT Spring Cloud Contract Dredd So, let's get started, but before that if you've any doubts on what all you can achieve with contract testing and all its know-hows, make sure to check out this blog: How to Perform PACT Contract Testing: A Step-by-Step Guide ( hypertest.co ) What is Contract Testing? In software development, complex systems are often built from interacting components. Contract testing establishes a well-defined interface, similar to an API, that governs communication between these components. This interface specifies the expected behavior of each component, including: Data formats: The structure and validation rules for data exchanged between components. Message specifications : The format and content of messages used for communication. Error handling: How errors and exceptions should be communicated and managed. By defining and enforcing these contracts, contract testing ensures that components can interact seamlessly, regardless of their internal implementation details. This promotes: loose coupling, reduces integration complexity, and facilitates independent development and deployment of components. What is consumer-driven contract testing? Consumer-Driven Contract Testing is the widely accepted approach of performing contract testing. Basically, there are two parties involved in a contract, one asking for the data( consumer ) and the other one providing the data( provider ). Here, the consumer of the service dictates the terms of the contract. It tells the provider what it expects in terms of data format and structure. The provider then ensures that it can meet these expectations. This approach has several benefits: Flexibility: Consumers define their requirements, leading to more flexibility and less risk of miscommunication. Independence: Teams can work independently on their services, as long as they adhere to the agreed contracts. Reduced Risk of Breakdowns: By ensuring that the provider meets the consumer's expectations, the risk of breakdowns in communication between services is significantly reduced. What are the Benefits of Contract Testing? Problems are caught during development, not after deployment. Teams can work on their services without constant coordination, as long as they adhere to the contracts. Ensures that as long as the contract is respected, services will interact seamlessly in production. Contract testing streamlines the integration and examination of microservices, making the process smoother. The upkeep of the system is simplified and becomes less burdensome. Contract testing allows for focused attention on individual modules. For instance, to assess module A's contract, there's no need for full integration with other modules; it can be evaluated on its own. Contract Testing Use Cases Contract testing stands out as an effective technique for verifying the dependability and interoperability of microservices and APIs. Nonetheless, it's important to note that it doesn't suit every testing need. Below, we outline several typical scenarios where contract testing is particularly beneficial: Use Case 1: User Authentication in a Social Media App ➡️ Scenario: In a social media application, there are two microservices: User Service and Post Service . The User Service handles user authentication, while the Post Service manages the creation and display of posts. ➡️ Contract: The contract specifies that when the Post Service receives a user ID, it sends a request to the User Service to authenticate the user and receive basic user profile data. Example Contract (JSON format): { "request": { "path": "/authenticateUser", "method": "POST", "body": { "userId": "string" } }, "response": { "status": 200, "body": { "userId": "string", "userName": "string", "isAuthenticated": "boolean" } } } ➡️ Testing the Contract: User Service: Tests to ensure it can process the authentication request and return user data in the correct format. Post Service: Tests to verify it sends the correct user ID format and handles the received user data appropriately. Understanding this use case, we can say that contract testing ensures that the microservices can reliably communicate with each other, adhering to the predefined contracts, which is vital for the smooth functioning of complex, distributed systems. API Contract Testing Tools in 2024 API Contract Testing Tools are essential in modern software development, especially when dealing with microservices architectures. These tools help ensure that APIs behave as expected and adhere to their defined contracts. We have covered both the free tools and the paid tools in the API Contract Testing category. The top 4 best performing API Contract Testing Tools to consider for 2024 are: HyperTest PACT Spring Cloud Contract Dredd Feature HyperTest Pact Spring Cloud Contract Dredd Type Dedicated Contract Testing Tool Open-Source Contract Testing Tool Contract Testing Framework (for Spring Cloud) API Documentation Tool (with testing capabilities) Focus Contract verification through request recording and replay Consumer-driven contract definition and verification Contract testing within Spring ecosystem API documentation testing Implementation SDK integration within backend services Separate consumer and provider contracts Annotations within Spring code or separate files CLI tool for running tests against API documentation Contract Definition Recorded requests and expected responses Consumer-defined expectations (stubs/mocker) Annotations defining producer and consumer contracts DSL (Domain Specific Language) for defining API behavior Test Generation Automatic test generation based on recorded traffic Manual or assisted test creation Generates tests based on annotations Generates tests based on API documentation Asynchronous Support Yes (message queues like RabbitMQ) Limited Yes (message queues) Not directly supported Database Testing Yes (verification of data calls) No No Not directly supported Mocking Auto-mocks all dependencies during test execution Requires separate mocking framework Mocks external dependencies during test execution Not directly for testing Test Coverage Reports code coverage on functional as well as integration layer Limited coverage reports Reports coverage based on contract annotations Not directly for testing 1. HyperTest - API Contract Testing Tool HyperTest is a modern tool specifically designed for API contract testing . It offers robust capabilities for ensuring that APIs meet their specified contracts. Key Features: ✔️ Test GraphQL, gRPC & REST APIs ✔️ Test Queues/Async flows and contracts for 3rd Party APIs ✔️ Test message queues & autonomous database testing ✔️ Automatic assertions on both data and schema ✔️ Code Coverage Reports for both core functions as well as integration layer ✔️ Integration with any CI/CD tool like Jenkins, Circle CI, GitLab etc Schedule A Demo 2. Pact - API Contract Testing Tool Pact is a popular open-source tool for contract testing . It focuses on the interactions between consumer and provider by defining and verifying HTTP requests and responses. Key Features: ✔️Consumer-Driven Contracts: Pact allows the consumer to define the expected behavior of the provider, which can then be verified by the provider. ✔️Mock Service: It provides a mock service for the consumer to interact with during testing, ensuring that the consumer's requests match the contract. ✔️Integration with CI/CD: Pact integrates seamlessly with continuous integration/continuous deployment pipelines, enhancing the development workflow. ✔️Language Support: Offers wide language support including Ruby, JVM languages (Java, Kotlin, Scala), NET, JavaScript, Swift, and more. 3. Spring Cloud Contract - API Contract Testing Tool Designed for Spring applications, this tool is used for implementing Consumer-Driven Contract (CDC) testing . Key Features: ✔️Integration with Spring: Perfect for applications built with the Spring framework. ✔️Stub Runner: Automatically generates stubs for the consumer, which can be used for tests. ✔️Supports Messaging: Apart from HTTP, it also supports contract testing for asynchronous messaging. 4. Dredd - API Contract Testing Tool Dredd is a language-agnostic HTTP API testing tool that validates whether an API implementation adheres to its documentation. Key Features: ✔️Support for API Blueprint and OpenAPI: Works with API Blueprint and OpenAPI specifications. ✔️Hooks: Offers hooks in several languages to set up preconditions or clean up after tests. ✔️Continuous Integration: Easy integration with CI tools and services. Conclusion Each of these tools has its strengths and fits different needs in the API development lifecycle. The choice of tool often depends on the specific requirements of the project, such as the programming language used, integration capabilities, and the complexity of the API interactions. By employing these tools effectively, teams can ensure more reliable and robust API communication within their applications. Here is a detailed comparison chart of the most widely used contract testing tools, click here to get to know those tools more technically. Check out our other contract testing resources for a smooth adoption of this highly agile and proactive practice in your development flow: Tailored Approach to Test Microservices Comparing Pact Contract Testing and Hypertest Checklist For Implementing Contract Testing Related to Integration Testing Frequently Asked Questions 1. Which tool is used for contract-driven testing? Pact is a popular tool for contract-driven testing, ensuring seamless integration in distributed systems. It allows teams to define and manage contracts, validating interactions between services to prevent issues caused by changes. Pact supports various languages and frameworks, making it versatile for diverse technology stacks. 2. Is contract testing same as API testing? No, contract testing and API testing differ. API testing examines the functionality and performance of an API, while contract testing focuses on verifying agreements or contracts between services to ensure they communicate correctly. Contract testing validates interactions and expectations, enhancing compatibility in distributed systems. 3. What is the basic of contract testing? Contract testing ensures seamless integration in distributed systems. It relies on predefined agreements or contracts specifying expected behaviors between software components. These contracts serve as benchmarks for validation, preventing issues caused by changes during development. For your next read Dive deeper with these related posts! 07 Min. Read Contract Testing for Microservices: A Complete Guide Learn More 06 Min. Read What is Consumer-Driven Contract Testing (CDC)? Learn More 04 Min. Read Contract Testing: Microservices Ultimate Test Approach Learn More

Airmeet's test cases, based on outdated mockups, missed crucial bugs (like key_added/removed) due to a disconnect with real user journeys. Their search for the new solution was to mimic real interactions that can help them identify and fix issues faster, improving the customer experience. Customer Success Airmeet and HyperTest: A Partnership to Erase 70% Outdated Mocks and Enhance Testing Speed By 80% Airmeet's test cases, based on outdated mockups, missed crucial bugs (like key_added/removed) due to a disconnect with real user journeys. Their search for the new solution was to mimic real interactions that can help them identify and fix issues faster, improving the customer experience. Pain Points: Outdated mocks caused integration problems between testing and production. Slow manual testing slowed down releases. Maintaining tests took time away from development. Results: Test with mocks that update automatically, making tests reliable. Slashed regression testing time from days to hours, speeding releases. Boosted code coverage to 75-85%, without writing or maintaining test scripts. About: Founded: 2019 Industry: Virtual Event Platforms Airmeet, established in 2019 in Bangalore, India, quickly became a leading name in the virtual event platform industry, achieving unicorn status within two years. The platform is designed to deliver a fully immersive virtual event experience, simulating real-life interactions through features like interactive polls, Q&A sessions, breakout rooms, and customizable virtual backgrounds. To date, Airmeet has facilitated over 150 million minutes of video airtime and served more than 120,000 event organizers worldwide. Airmeet's Requirements: Develop a testing solution to manage frequent updates efficiently, without the overhead of continuous manual testing. Enhance integration testing speed and efficiency to improve system performance and readiness for releases. Overcome the limitations posed by outdated mocks that compromised test accuracy and trustworthiness. Challenge: As Airmeet expanded, the complexity of its virtual event platform required a more effective testing strategy. Heavy reliance on APIs and the fast pace of development made traditional manual testing methods impractical. The use of outdated mocks in testing further complicated the issue, as they often resulted in a significant mismatch between testing scenarios and actual operational conditions, leading to critical bugs in the live environment. Manual testing processes could not keep up with the platform’s scale and the rapid pace of development, leading to increased costs and delayed product releases. The use of outdated mocks in unit and integration tests led to unexpected bugs into production. Existing test suite required a lot of maintenance that moved away precious time from development . Solution: To streamline these challenges, Airmeet adopted HyperTest, an advanced integration testing tool that automated their testing processes effectively. HyperTest's capabilities were integrated swiftly into Airmeet's core services, offering double-digit code coverage across all major services and significantly less manual testing. HyperTest automated the generation and execution of test cases, drastically reducing the team's workload. It ensured that all changes underwent rigorous testing and received approval automatically before being moved to production, enhancing the quality and speed of releases. HyperTest's ability to mock out all the dependent services, third-party APIs and databases help ed in eliminating the need to write and maintain mocks, making testing of integration scenarios reliable with consistent results . HyperTest has been a game-changer for us in API regression testing. It has significantly saved time and effort by green-lighting changes before they go live with our weekly releases. -Vinay Jaasti, Chief Technology Officer Read it now How Yellow.ai Employs HyperTest to Achieve 95% API Coverage and Ensure a Flawless Production Environment Read it now Processing 1.5 Million Orders, Zero Downtime: How Nykaa Optimizes with HyperTest View all Customers Catch regressions in code, databases calls, queues and external APIs or services Take a Live Tour Book a Demo

Unit tests aren't enough. Learn how real integration testing prevents costly production failures. 10 Min. Read 20 March 2025 Why your Tests Pass but Production Fails? Vaishali Rastogi WhatsApp LinkedIn X (Twitter) Copy link Executive Summary: Integration testing is not just complementary to unit testing—it's essential for preventing catastrophic production failures. Organizations implementing robust integration testing report 72% fewer critical incidents and 43% faster recovery times. This analysis explores why testing components in isolation creates a dangerous false confidence and how modern approaches can bridge the gap between test and production environments. As software systems grow increasingly complex and distributed, the gap between isolated test environments and real-world production becomes more treacherous. At HyperTest, we've observed this pattern across organizations of all sizes, leading us to investigate the limitations of isolation-only testing approaches. For this deep dive, I spoke with engineering leaders and developers across various organizations to understand how they navigate the delicate balance between unit and integration testing. Their insights reveal a consistent theme: while unit tests provide valuable guardrails, they often create a false sense of security that can lead to catastrophic production failures. Why Integration Testing Matters? Integration testing bridges the gap between isolated components and real-world usage. Unlike unit tests, which verify individual pieces in isolation, integration tests examine how these components work together—often revealing issues that unit tests simply cannot detect. As Vineet Dugar, a senior architect at a fintech company, explained: "In our distributed architecture, changes to a single system can ripple across the entire platform. We've learned the hard way that verifying each component in isolation isn't enough—we need to verify the entire system works holistically after changes." This sentiment was echoed across all our interviews, regardless of industry or company size. The Isolation Illusion When we test in isolation, we create an artificial environment that may not reflect reality. This discrepancy creates what I call the "Isolation Illusion"—the false belief that passing unit tests guarantees production reliability. Consider this Reddit comment from a thread on r/programming: "We had 98% test coverage, all green. Deployed on Friday afternoon. By Monday, we'd lost $240K in transactions because our payment processor had changed a response format that our mocks didn't account for. Unit tests gave us confidence to deploy without proper integration testing. Never again." - u/DevOpsNightmare This experience highlights why testing in isolation, while necessary, is insufficient. Common Integration Failure Points Integration testing exposes critical vulnerabilities that unit tests in isolation simply cannot detect. Based on our interviews, here are the most frequent integration failure points that isolation testing misses: Failure Point Description Real-World Impact Schema Changes Database or API schema modifications Data corruption, service outages Third-Party Dependencies External API or service changes Failed transactions, broken features Environment Variables Configuration differences between environments Mysterious failures, security issues Timing Assumptions Race conditions, timeouts, retry logic Intermittent failures, data inconsistency Network Behavior Latency, packet loss, connection limits Timeout cascades, degraded performance 1. Schema Changes: The Silent Disruptors Schema modifications in databases or APIs represent one of the most dangerous integration failure points. These changes can appear harmless in isolation but cause catastrophic issues when systems interact. u/DatabaseArchitect writes: "We deployed what seemed like a minor schema update that passed all unit tests. The change added a NOT NULL constraint to an existing column. In isolation, our service worked perfectly since our test data always provided this field. In production, we discovered that 30% of requests from upstream services didn't include this field - resulting in cascading failures across five dependent systems and four hours of downtime." Impact scale: Schema changes have caused data corruption affecting millions of records, complete service outages lasting hours, and in financial systems, reconciliation nightmares requiring manual intervention. Detection challenge: Unit tests with mocked database interactions provide zero confidence against schema integration issues, as they test against an idealized version of your data store rather than actual schema constraints. 2. Third-Party Dependencies: The Moving Targets External dependencies change without warning, and their behavior rarely matches the simplified mocks used in unit tests. u/PaymentEngineer shares: "Our payment processor made a 'minor update' to their API response format - they added an additional verification field that was 'optional' according to their docs. Our mocked responses in unit tests didn't include this field, so all tests passed. In production, their system began requiring this field for certain transaction types. Result: $157K in failed transactions before we caught the issue." Impact scale: Third-party integration failures have resulted in transaction processing outages, customer-facing feature breakages, and compliance violations when critical integrations fail silently. Detection challenge: The gap between mocked behavior and actual third-party system behavior grows wider over time, creating an increasing risk of unexpected production failures that no amount of isolated testing can predict. 3. Environment Variables: Configuration Chaos Different environments often have subtle configuration differences that only manifest when systems interact in specific ways. u/CloudArchitect notes: "We spent two days debugging a production issue that didn't appear in any test environment. The root cause? A timeout configuration that was set to 30 seconds in production but 120 seconds in testing. Unit tests with mocks never hit this timeout. Integration tests in our test environment never triggered it. In production under load, this timing difference caused a deadlock between services." Impact scale: Configuration discrepancies have caused security vulnerabilities (when security settings differ between environments), mysterious intermittent failures that appear only under specific conditions, and data processing inconsistencies. Detection challenge: Environment parity issues don't show up in isolation since mocked dependencies don't respect actual environment configurations, creating false confidence in deployment readiness. 4. Timing Assumptions: Race Conditions and Deadlocks Asynchronous operations and parallel processing introduce timing-related failures that only emerge when systems interact under real conditions. u/DistributedSystemsLead explains: "Our system had 99.8% unit test coverage, with every async operation carefully tested in isolation. We still encountered a race condition in production where two services would occasionally update the same resource simultaneously. Unit tests never caught this because the timing needed to be perfect, and mocked responses didn't simulate the actual timing variations of our cloud infrastructure." Impact scale: Timing issues have resulted in data inconsistency requiring costly reconciliation, intermittent failures that frustrate users, and in worst cases, data corruption that propagates through dependent systems. Detection challenge: Race conditions and timing problems typically only appear under specific load patterns or environmental conditions that are nearly impossible to simulate in isolation tests with mocked dependencies. 5. Network Behavior: The Unreliable Foundation Network characteristics like latency, packet loss, and connection limits vary dramatically between test and production environments. u/SREVeteran shares: "We learned the hard way that network behavior can't be properly mocked. Our service made parallel requests to a downstream API, which worked flawlessly in isolated tests. In production, we hit connection limits that caused cascading timeouts. As requests backed up, our system slowed until it eventually crashed under its own weight. No unit test could have caught this." Impact scale: Network-related failures have caused complete system outages, degraded user experiences during peak traffic, and timeout cascades that bring down otherwise healthy services. Detection challenge: Most unit tests assume perfect network conditions with instantaneous, reliable responses - an assumption that never holds in production environments, especially at scale. 6. Last-Minute Requirement Changes: The Integration Nightmare Radhamani Shenbagaraj, QA Lead at a healthcare software provider, shared: "Last-minute requirement changes are particularly challenging. They often affect multiple components simultaneously, and without proper integration testing, we've seen critical functionality break despite passing all unit tests." Impact scale: Rushed changes have led to broken critical functionality, inconsistent user experiences, and data integrity issues that affect customer trust. Detection challenge: When changes span multiple components or services, unit tests can't validate the entire interaction chain, creating blind spots exactly where the highest risks exist. These challenges highlight why the "works on my machine" problem persists despite extensive unit testing. True confidence comes from validating how systems behave together, not just how their individual components behave in isolation. As one senior architect told me during our research: "Unit tests tell you if your parts work. Integration tests tell you if your system works. Both are necessary, but only one tells you if you can sleep soundly after deploying." The Hidden Cost of Over-Mocking One particularly troubling pattern emerged from our interviews: the tendency to over-mock external dependencies creates a growing disconnect from reality. Kiran Yallabandi from a blockchain startup explained: "Working with blockchain, we frequently encounter bugs related to timing assumptions and transaction processing. These issues simply don't surface when dependencies are mocked—the most catastrophic failures often occur at the boundaries between our system and external services." The economics of bug detection reveal a stark reality: Cost to fix a bug in development: $100 Cost to fix a bug in QA: $500 Cost to fix a bug in production: $5,000 Cost to fix a production integration failure affecting customers: $15,000+ The HyperTest Approach: Solving Integration Testing Challenges All these challenges mentioned above clearly reflects how integration testing can be a tricky thing to achieve, but now coming to our SDK’s approach which addresses many of the challenges our interviewees highlighted. The HyperTest SDK offers a promising solution that shifts testing left while eliminating common integration testing hurdles. "End-to-end Integration testing can be conducted without the need for managing separate test environments or test data, simplifying the entire integration testing process." This approach aligns perfectly with the pain points our interviewees described, let’s break them down here: 1. Recording real traffic for authentic tests Instead of relying on artificial mocks that don't reflect reality, HyperTest captures actual application traffic: The SDK records real-time interactions between your application and its dependencies Both positive and negative flows are automatically captured, ensuring comprehensive test coverage Tests use real production data patterns, eliminating the "isolation illusion" 2. Eliminating environment parity problems Vineet Dugar mentioned environment discrepancies as a major challenge. HyperTest addresses this directly: "Testing can be performed autonomously across production, local, or staging environments, enhancing flexibility while eliminating environment management overhead." This approach allows teams to: Test locally using production data flows Receive immediate feedback without deployment delays Identify integration issues before they reach production 3. Solving the test data challenge Several interviewees mentioned the difficulty of generating realistic test data. The HyperTest approach: Records actual user flows from various environments Reuses captured test data, eliminating manual test data creation Automatically handles complex data scenarios with nested structures Striking the Right Balance Integration testing doesn't replace unit testing—it complements it. Based on our interviews and the HyperTest approach, here are strategies for finding the right balance: Map Your System Boundaries Identify where your system interfaces with others and prioritize integration testing at these boundaries. Prioritize Critical Paths Not everything needs comprehensive integration testing. Focus on business-critical paths first. Implement Contract Testing As Maheshwaran, a DevOps engineer at a SaaS company, noted: "Both QAs and developers share responsibility for integration testing. We've found contract testing particularly effective for establishing clear interfaces between services." Monitor Environment Parity Vineet Dugar emphasized: "Environment discrepancies—differing environment variables or dependency versions—are often the root cause of the 'works on my machine' syndrome. We maintain a configuration drift monitor to catch these issues early." From 3 Days to 3 Hours: How Fyers Transformed Their Integration Testing? Fyers, a leading financial services company serving 500,000+ investors with $2B+ in daily transactions, revolutionized their integration testing approach with HyperTest. Managing 100+ interdependent microservices, they reduced regression testing time from 3-4 days to under 3 hours while achieving 85% test coverage. "The best thing about HyperTest is that you don't need to write and maintain any integration tests. Also, any enhancements or additions to the APIs can be quickly tested, ensuring it is backwards compatible." - Khyati Suthar, Software Developer at Fyers Read the complete Fyers case study → Identifying Integration Test Priorities One of the most valuable insights from the HyperTest approach is its solution to a common question from our interview subjects: "How do we know what to prioritize for integration testing?" The HyperTest SDK solves this through automatic flow recording: "HyperTest records user flows from multiple environments, including local and production, generating relevant test data. Tests focus on backend validations, ensuring correct API responses and database interactions through automated assertions." This methodology naturally identifies critical integration points by: Capturing Critical Paths Automatically By recording real user flows, the system identifies the most frequently used integration points. Identifying Both Success and Failure Cases "Captured API traffic includes both successful and failed registration attempts... ensuring that both negative and positive application flows are captured and tested effectively." Targeting Boundary Interactions The SDK focuses on API calls and database interactions—precisely where integration failures are most likely to occur. Prioritizing Based on Real Usage Test cases reflect actual system usage patterns rather than theoretical assumptions. Strategic approaches to Integration testing Integration testing requires a different mindset than unit testing. Based on our interviewees' experiences and the HyperTest approach, here are strategic approaches that have proven effective: 1. Shift Left with Recording-Based Integration Tests The HyperTest methodology demonstrates a powerful "shift left" approach: "Implementing tests locally allows developers to receive immediate feedback, eliminating wait times for deployment and QA phases." This addresses Radhamani Shenbagaraj's point about last-minute changes affecting functionality and deadlines. With a recording-based approach, developers can immediately see the impact of their changes on integrated systems. 2. Focus on Realistic Data Without Management Overhead HyperTest solves a critical pain point our interviewees mentioned: "Using production data for testing ensures more realistic scenarios, but careful selection is necessary to avoid complications with random data generation." The recording approach automatically captures relevant test data, eliminating the time-consuming process of creating and maintaining test data sets. 3. Automate External Dependency Testing The HyperTest webinar highlighted another key advantage: "HyperTest automates the mocking of external dependencies, simplifying the testing of interactions with services like databases." This directly addresses Kiran Yallabandi's concern about blockchain transaction timing assumptions—by capturing real interactions, the tests reflect genuine external service behaviors. Eliminating environment parity issues Environment inconsistencies frequently cause integration failures that unit tests cannot catch. Vineet Dugar highlighted: "Environment parity can cause issues—environment variable discrepancies, dependency discrepancies, etc." The HyperTest approach offers an innovative solution: "End-to-end testing can be conducted locally without asserting business logic or creating separate environments." This eliminates the test environment ownership confusion that the webinar noted as a common challenge: "Ownership of test environments creates confusion among development, QA, and DevOps teams, leading to accountability issues." Creating a culture of Integration testing Technology alone isn't enough. Our interviews revealed that creating a culture that values integration testing is equally important: 1. Shared Responsibility with Reduced Overhead Integration testing has traditionally been a point of friction between development and QA teams. Yet our interviews with engineering leaders reveal a critical insight: when developers own integration testing, quality improves dramatically. As Maheshwaran pointed out: "Both QAs and Devs are responsible for performing integration testing." The HyperTest approach takes this principle further by specifically empowering developers to own integration testing within their workflow. Here's why this creates superior outcomes: Contextual Understanding : Developers possess deep contextual knowledge of how code should function. When they can directly verify integration points, they identify edge cases that would be invisible to those without implementation knowledge. Immediate Feedback Loops : Rather than waiting for downstream QA processes, developers receive instant feedback on how their changes impact the broader system. The HyperTest SDK achieves this by executing integration tests locally during development. Reduced Context Switching : When developers can run integration tests without environment setup overhead, they integrate testing into their daily workflow without disrupting their productive flow. Detection of integration issues occurs 3.7x earlier in the development cycle 2. Realistic Time Allocation Through Automation Radhamani Shenbagaraj noted: "Requirements added at the last-minute affect functionality and deadlines." The HyperTest recording-based approach addresses this by: "Automating complex scenarios... particularly with nested structures." This automation significantly reduces the time required to implement and maintain integration tests. 3. Root Cause Analysis for Faster Resolution The HyperTest webinar highlighted how their approach: "Provides root cause analysis by comparing code changes to the master branch, identifying failure scenarios effectively." This facilitates a learning culture where teams can quickly identify and resolve integration issues. Combining approaches for optimal Integration testing Based on our research, the most effective integration testing strategies combine: Traditional integration testing techniques for critical components Contract testing for establishing clear API expectations Recording-based testing to eliminate environment and data management challenges Chaos engineering for resilience testing Continuous monitoring to detect integration issues in production As one interviewee noted: The closer your test environment matches production, the fewer surprises you'll encounter during deployment. The HyperTest approach takes this a step further by using actual production behavior as the basis for tests, eliminating the gap between test and production environments. Beyond the Isolation Illusion The isolation illusion—the false confidence that comes from green unit tests—has caused countless production failures. As our interviews revealed, effective testing strategies must include both isolated unit tests and comprehensive integration tests. Vineet Dugar summarized it perfectly: "In a distributed architecture, changes to one system ripple across the entire platform. We've learned that verifying components in isolation simply isn't enough." Modern approaches like HyperTest's recording-based methodology offer promising solutions to many of the traditional challenges of integration testing: Eliminating test environment management Removing test data creation and maintenance overhead Automatically identifying critical integration points Providing immediate feedback to developers By focusing on system boundaries, critical user journeys, and authentic system behavior, teams can develop integration testing strategies that provide genuine confidence in system behavior. Key Takeaways The Isolation Illusion is Real : 92% of critical production failures occur at integration points despite high unit test coverage Schema Changes and Third-Party Dependencies are the leading causes of integration failures Recording Real Traffic provides dramatically more authentic integration tests than artificial mocks Environment Parity Problems can be eliminated through local replay capabilities Shared Responsibility between developers and QA leads to 3.7x earlier detection of integration issues Ready to eliminate your integration testing headaches? Schedule a demo of HyperTest's recording-based integration testing solution at hypertest.co/demo Special thanks to Vineet Dugar , Maheshwaran , Kiran Yallabandi , Radhamani Shenbagaraj , and the other engineering leaders who contributed their insights to this article. Prevent Logical bugs in your databases calls, queues and external APIs or services Take a Live Tour Book a Demo

To avoid costly implications, an application's complexity requires early defect detection. In this whitepaper, discover how HyperTest helps developers sign off releases in minutes. How can HyperTest help green-light a new commit in less than 5 mins To avoid costly implications, an application's complexity requires early defect detection. In this whitepaper, discover how HyperTest helps developers sign off releases in minutes. Download now Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

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Discover common challenges in microservices architecture. Explore strategies to overcome complexities and ensure successful implementation. 30 May 2023 07 Min. Read Managing & Deploying Microservices: Key Challenges Download the 101 Guide WhatsApp LinkedIn X (Twitter) Copy link Fast Facts Get a quick overview of this blog Transitioning from monolithic to microservices architecture is fueled by the demand for flexibility, scalability, and rapid deployment. Microservices offer benefits like increased resilience, faster delivery, enhanced scalability, and quicker time-to-market. Dynamic Management & Ensuring Efficiency: Addressing service discovery, monitoring, scalability, fault tolerance, testing, and security in cloud-based microservices. Automation, Containerization, CI/CD, and Collaboration: Implement automated processes and encourage teamwork. Download the 101 Guide The trend of transitioning from monolithic applications to microservices is gaining momentum, with many technology leaders embarking on this modernization initiative. Microservices represent a software development approach where applications are divided into smaller, autonomous components. This style of architecture has become popular among businesses all over the world, especially those that want to speed up the delivery process and increase the rate of deployment. Microservices offer several benefits, including improved resilience, faster delivery, enhanced scalability, and quicker time-to-market. Microservices are becoming a big deal in the software development industry because of the growing need for more flexible, scalable, and reliable software applications. While microservices offer many benefits, they also come with several challenges that can make managing and deploying them difficult. In this blog, we are going to explore the problems that arise when deploying these independent services to production. Quick Question Microservice integration bugs got you down? We can help! Yes Challenges of Managing and Deploying Microservices Architecture When you deploy a monolithic application , you run multiple copies of a single, usually large application that are all the same. Most of the time, you set up N physical or virtual servers and run M copies of the application on each one. Putting a monolithic application into use isn't always easy, but it's much easier than putting a microservices application into use. A microservices application consists of hundreds or even thousands of services. They’re written in a variety of languages and frameworks. Each one is like a small application that has its own deployment, resource, scaling, and monitoring needs. Even more difficult is that services must be deployed quickly, reliably, and at a low cost, even though they are complicated. Managing and deploying microservices can be hard for teams of different sizes in different ways, depending on how well the microservice boundaries are set and how well the inter-service dependencies are set. Let’s look at some of the most common problems that teams encounter when managing their multi-repo architecture. a) Service Discovery Working on a microservices application requires you to manage service instances with dynamic locations. Depending on things like auto-scaling, service upgrades, and failures, it may be necessary to make changes to these instances while they are running. In such a case, these instances' dependent services must be informed. Suppose you are developing code that invokes a REST API service. To make a request, the code requires the IP address and port of the service instance. In a microservices architecture, the number of instances will vary, and their locations will not be specified in a configuration file. Therefore, it is difficult to determine the number of services at a given time. In a cloud-based microservices environment, where network locations are assigned on the fly, service discovery is needed to find service instances in those locations . One way to tackle this challenge is by using a service registry that keeps track of all the available services in the system. For instance, microservices-based applications frequently use Netflix's Eureka as a service registry. b) Monitoring and Observability Services in a multi-repo system communicate with each other in order to serve the business purpose they’re entitled to do. The calls between services can penetrate deep through many layers, making it hard to understand how they depend on each other. In such a situation, monitoring and observability are required. They both, when combined, work as a proactive approach and an aid while doing RCA. But in a microservices architecture, it can be challenging to monitor and observe the entire system effectively. In a traditional monolithic application, monitoring can be done at the application level. However, in a microservices-based application, monitoring needs to be done at the service level . Each microservice needs to be monitored independently, and the data collected needs to be aggregated to provide a holistic view of the system, which can be challenging. In 2019, Amazon experienced a major outage in their AWS Elastic Load Balancer service. The issue was caused by a problem with the monitoring system, which failed to detect the issue in time, leading to a prolonged outage. To monitor and observe microservices effectively, organizations need to use specialized monitoring tools that can provide real-time insights into the entire system's health. These tools need to be able to handle the large volume of data generated by the system and be able to correlate events across different services. Every service/ component/ server should be monitored. Suppose a service talks to 7 other services before giving out a response, so tracing the complete path it followed becomes super critical to monitor and log to know the root cause of failure. c) Scalability Switching to microservices makes it possible to scale in a big way, but that makes it harder to manage them. Rightly allocating resources and the ability to scale up or scale down when in demand put forward a major concern. Rather than managing a single application running on a single server, or spread across several servers with load-balancing, the current scenario involves managing various application components written in different programming languages, operating on diverse hardware, running on separate virtualization hypervisors, and deployed across multiple on-premise and cloud locations. To handle increased demand for the application, it's essential to coordinate all underlying components to scale, or identify which components need to be scaled. There might be scenarios where a service is heavily loaded with traffic and needs to be scaled up in order to match the increased demand. It is even more crucial to make sure that the entire system remains responsive and resilient during the scaling process . Fault Tolerance Each microservice is designed to perform a specific function and communicates with other microservices to deliver the final product or service. In a poorly designed microservices infrastructure, any failure or disruption in one microservice can affect the entire system's performance, leading to downtime, errors, or data loss. d) Testing Testing becomes super complex when it comes to microservices. Each service needs to be tested individually, and there needs to be a way to test the interactions between services. Microservices architecture is designed to allow continuous integration and deployment, which means that updates are frequently made to the system. This can also make it difficult to test and secure the system as a whole because changes are constantly being made. One common way to test microservices is to use contract testing , which involves using predefined contracts to test how the services interact with each other. HyperTest is a popular tool that follows contract testing framework to test microservices-based applications. Additionally, testing needs to be automated, and it needs to be done continuously to ensure that the system is functioning correctly. Since rapid development is inherent to microservices, teams must test each service separately and in conjunction, to evaluate the overall stability and quality of such distributed systems. e) Security Each service needs to be secured individually, and the communication between services needs to be secure. Additionally, there needs to be a centralized way to manage access control and authentication across all services. According to a survey conducted by NGINX, security is one of the biggest challenges that organizations face when deploying microservices. One popular approach to securing microservices is using API gateways , which act as a proxy between the client and the microservices. API gateways can perform authentication and authorization checks, as well as rate limiting and traffic management. Kong is a popular API gateway that can be used to secure microservices-based applications. Conclusion To effectively handle these challenges, organizations must adopt appropriate strategies, tools, and processes. This includes implementing automation, containerization, and continuous integration and deployment (CI/CD) practices. Additionally, it is essential to have a strong collaboration between teams, as well as comprehensive testing and monitoring procedures. With careful planning and execution, microservices architecture can help organizations achieve their goals of faster delivery, better scalability, and improved customer experiences. We have compiled extensive research into one of our whitepaper, titled " Testing Microservices ,” to address this significant obstacle presented by microservices. Check it out to learn the tried-and-true method that firms like Atlassian, SoundCloud, and others have used to solve this issue. Community Favourite Reads Unit tests passing, but deployments crashing? There's more to the story. Learn More Masterclass on Contract Testing: The Key to Robust Applications Watch Now Related to Integration Testing Frequently Asked Questions 1. What is Microservices Architecture? Microservices architecture is a way of building software where you break it into tiny, separate pieces, like building with Lego blocks. Each piece does a specific task and can talk to the others. It makes software more flexible and easier to change or add to because you can work on one piece without messing up the whole thing. 2. Why use microservices? Microservices are used to create software that's flexible and easy to manage. By breaking an application into small, independent pieces, it becomes simpler to develop and test. This approach enables quick updates and better scalability, ensuring that if one part fails, it doesn't bring down the whole system. Microservices also work well with modern cloud technologies, helping to reduce costs and make efficient use of resources, making them an ideal choice for building and maintaining complex software systems. 3. What are the benefits of Microservices Architecture? Microservices architecture offers several advantages. It makes software easier to develop, test, and maintain because it's divided into small, manageable parts. It allows for faster updates and scaling, enhancing agility. If one part breaks, it doesn't affect the whole system, improving fault tolerance. Plus, it aligns well with modern cloud-based technologies, reducing costs and enabling efficient resource usage. For your next read Dive deeper with these related posts! 10 Min. Read What is Microservices Testing? Learn More 08 Min. Read Microservices Testing Challenges: Ways to Overcome Learn More 07 Min. Read Scaling Microservices: A Comprehensive Guide Learn More

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Explore non-functional testing: its types, examples, and how it ensures software performance, security, and usability beyond functional aspects. 25 April 2024 09 Min. Read What is Non-Functional Testing? Types with Example WhatsApp LinkedIn X (Twitter) Copy link Download the Checklist What is Non-Functional Testing? Non-functional testing is an aspect of software development that assesses a system’s performance and usability. It focuses on the broader aspects of a system’s behavior under various conditions thus differing from functional testing which evaluates only specific features. Non-functional testing encompasses areas such as performance testing, usability testing, reliability testing, and scalability testing among others. It guarantees that a software application not only functions correctly but also delivers user expectations with respect to speed, responsiveness and overall user experience. It is essential in identifying vulnerabilities and areas for improvement in a system’s non-functional attributes. If performed early in the development lifecycle. it helps in enhancing the overall quality of the software thereby meeting performance standards and user satisfaction. Why Non-Functional Testing? Non-functional testing is important for organizations aiming to deliver high-quality software that goes beyond mere functional correctness. It is imperative for non-functional testing to assess aspects like performance, reliability, usability and scalability. Organizations can gain valuable insights into the performance of their software under various conditions this way, ensuring it meets industry standards and user expectations. ➡️ Non-functional testing helps with the identification and addressing of issues related to system performance, guaranteeing optimal speed and responsiveness. Organizations can use non-functional testing to validate the reliability of their software, which ensures stability of the same. ➡️ Usability testing, a key component of non-functional testing, ensures that the user interface is intuitive, ultimately enhancing user satisfaction. Scalability testing assesses a system's ability to handle growth, providing organizations with the foresight to accommodate increasing user demands. ➡️ Applying non-functional testing practices early in the software development lifecycle allows organizations to proactively address performance issues, enhance user experience and build strong applications. Non-functional testing requires an investment and organizations that do so can bolster their reputations for delivering high-quality software which minimizes the risks of performance-related issues. Non-Functional Testing Techniques Various techniques are employed by non-functional testing to evaluate the performance of the software among other things. One prominent technique within non-functional testing is performance testing, which assesses the system's responsiveness, speed, and scalability under different workloads. This proves to be vital for organisations that aim to ensure optimal software performance. ✅ Another technique is reliability testing which focuses on the stability and consistency of a system, ensuring it functions flawlessly over extended periods. ✅ Usability testing is a key technique under the non-functional testing umbrella, concentrating on the user interface's intuitiveness and overall user experience. This is indispensable for organisations to produce the best software. ✅ Scalability testing evaluates the system’s capacity to handle increased loads, providing insights into its ability to adapt to user demands. The application of a comprehensive suite of non-functional testing techniques ensures that the software not only meets basic requirements but also exceeds user expectations and industry standards, ultimately contributing to the success of the organization. Benefits of Non-Functional Testing Non-functional testing is a critical aspect of software development that focuses on evaluating the performance, reliability, and usability of a system beyond its functional requirements. This type of testing is indispensable for ensuring that a software application not only works as intended but also meets non-functional criteria. The benefits of non-functional testing are manifold, contributing significantly to the overall quality and success of a software product. Here are the benefits: Reliability: Non-functional testing enhances software system reliability by identifying performance issues and ensuring proper and consistent functionality under different environments. Scalability: It allows businesses to determine its ability to handle increased loads by assessing the system’s scalability. This ensures optimal performance as user numbers grow. Efficiency: To get faster response times and improved user experience, non-functional testing identifies and eliminates performance issues thereby improving the efficiency of applications. Security: The security of software systems is enhanced through non-functional testing by identifying vulnerabilities and weaknesses that could be exploited by malicious entities Compliance: It ensures compliance with industry standards and regulations, providing a benchmark for software performances and security measures. User Satisfaction: Non-functional testing addresses aspects like usability, reliability and performance. This contributes to a positive end-user experience. Cost-Effectiveness: Early detection and resolution of issues through testing results in cost savings by preventing post-deployment failures and expensive fixes. Optimized Resource Utilization: Non-functional testing helps in optimising resource utilisation by identifying areas where system resources may be under-utilised/overused, thus, enabling efficient allocation. Risk Mitigation: Non-functional testing reduces the risks associated with poor performance, security breaches, and system failures, enhancing the overall stability of software applications. Non-Functional Test Types Non-functional testing evaluates various aspects such as performance, security, usability, and reliability to ensure the software's overall effectiveness. Each non-functional test type plays a unique role in enhancing different facets of the software, contributing to its success in the market. We have already read about the techniques used. Let us focus on the types of non-functional testing. 1.Performance Testing: This acts as a measure for the software’s responsiveness, speed and efficiency under varying conditions. 2. Load Testing: Load testing acts as an evaluator for the system’s ability to handle specific loads, thereby ensuring proper performance during peak usage. 3. Security Testing: This identifies weaknesses, safeguarding the software against security threats and breaches which includes the leaking of sensitive data. 4. Portability Testing: Assesses the software's adaptability across different platforms and environments. 5. Compatibility Testing: Compatibility testing ensures smooth functionality across multiple devices, browsers and operating systems. 6. Usability Testing: To enhance the software’s usability, focus in this type of testing is on the user interface, navigation and overall user experience. 7. Reliability Testing: Reliability testing acts as an assurance for the software’s stability and dependability under normal and abnormal conditions. 8. Efficiency Testing: This evaluates resource utilisation which ensures optimal performance with the use of minimal resources. 9. Volume Testing: This tests the system’s ability to handle large amounts of data that is fed regularly to the system. 10. Recovery Testing: To ensure data integrity and system stability, recovery testing assesses the software’s ability to recover from all possible failures. 11. Responsiveness Testing: Responsiveness testing evaluates how quickly the system responds to inputs. 12. Stress Testing: This type of testing pushes the system beyond its normal capacity to identify its breaking points, thresholds and potential weaknesses. 13. Visual Testing: Visual testing focuses on the graphical elements to ensure consistency and accuracy in the software’s visual representation. A comprehensive non-functional testing strategy is necessary for delivering a reliable software product. Each test type addresses specific aspects that collectively contribute to the software's success in terms of performance, security, usability, and overall user satisfaction. Integrating these non-functional tests into the software development lifecycle is essential for achieving a high-quality end product that meets both functional and non-functional requirements. Advantages of Non-Functional Testing Non-functional testing has a major role to play in ensuring that a software application meets its functional, performance, security and usability requirements. These tests are integral for the delivery of a high-quality product that exceeds user expectations and withstands challenging environments. Here are some of the advantages of non-functional testing: 1.Enhanced Performance Optimization: Non-functional testing, particularly performance and load testing, allows organisations to identify and rectify issues with performance. It optimises the software's responsiveness and speed thus ensuring that the application delivers a hassle-free, smooth and efficient user experience under varying conditions and user loads. 2. Strong Security Assurance: With the sensitive nature of data in softwares being in question, security testing plays a key role in ensuring the safety of the same. Security testing is a major component of non-functional testing that helps organisations identify vulnerabilities and weaknesses in their software. By addressing these security concerns early in the development process, companies can safeguard sensitive data and protect against cyber threats thereby ensuring a secure product. 3. Improved User Experience (Usability Testing): Non-functional testing, such as usability testing, focuses on evaluating the user interface and user experience. By identifying and rectifying usability issues, organizations can enhance and promote the software's user-friendliness, resulting in increased customer satisfaction and loyalty. 4. Reliability and Stability Assurance: Non-functional testing, including reliability and recovery testing, guarantees the software's stability and dependability. By assessing how well the system handles failures and software setbacks and recovers from them, organizations can deliver a reliable product that instills confidence in users. 5. Cost-Efficiency Through Early Issue Detection: Detecting and addressing non-functional issues early in the development lifecycle can significantly reduce the cost of fixing problems post-release. By incorporating non-functional testing throughout the software development process, organizations can identify and resolve issues before they escalate, saving both time and resources. 6. Adherence to Industry Standards and Regulations: Non-functional testing ensures that a software product complies with industry standards, compliances and regulations. By conducting tests related to portability, compatibility, and efficiency, organisations can meet the necessary criteria, avoiding legal and compliance issues and ensuring a smooth market entry. The advantages of non-functional testing are manifold, ranging from optimizing performance and ensuring security to enhancing user experience and meeting industry standards. Embracing a comprehensive non-functional testing strategy is essential for organizations committed to delivering high-quality, reliable, and secure software products to their users. Limitations of Non-Functional Testing Non-functional testing, while essential for evaluation of software applications, is not without its limitations. These inherent limitations should be considered for the development of testing strategies that address both functional and non-functional aspects of software development. Here are some of the limitations of non-functional testing: Subjectivity in Usability Testing: Usability testing often involves subjective assessments that makes it challenging to quantify and measure the user experience objectively. Different users may have varying preferences which make it difficult to establish universal usability standards. Complexity in Security Testing: Security testing faces challenges due to the constantly changing nature of cyber threats. As new vulnerabilities arrive, it becomes challenging to test and protect a system against all security risks. Inherent Performance Variability: Performance testing results may differ due to factors like network conditions, hardware configurations, and third-party integrations. Achieving consistent performance across environments can be challenging. Scalability Challenges: While scalability testing aims to assess a system's ability to handle increased loads, predicting future scalability requirements accurately poses a task. The evolving nature of users’ demands makes it difficult to anticipate scalability needs effectively. Resource-Intensive Load Testing: Load testing, which involves simulating concurrent user loads, can be resource-intensive. Conducting large-scale load tests may require significant infrastructure, costs and resources, making it challenging for organizations with budget constraints. Difficulty in Emulating Real-Time Scenarios: Replicating real-time scenarios in testing environments can be intricate. Factors like user behavior, network conditions, and system interactions are challenging to mimic accurately, leading to incomplete testing scenarios. It is important for organizations to understand that these limitations help refine testing strategies, ensuring a balanced approach that addresses both functional and non-functional aspects. Despite these challenges, the use of non-functional testing remains essential for delivering reliable, secure, and user-friendly software products. Organisations should view these limitations as opportunities for improvement, refining their testing methodologies to meet the demands of the software development industry. Non-Functional Testing Tools Non-functional testing tools are necessary for the assessment of the performance, security, and other parts of software applications. Here are some of the leading tools that perform non-functional testing amongst a host of other tasks: 1.Apache JMeter: Apache JMeter is widely used for performance testing, load testing, and stress testing. It allows testers to simulate multiple users and analyze the performance of web applications, databases, and other services. 2. OWASP ZAP (Zed Attack Proxy): Focused on security testing, OWASP ZAP helps identify vulnerabilities in web applications. It automates security scans, detects potential threats like injection attacks, and assists in securing applications against common security risks. 3. LoadRunner: LoadRunner is renowned for performance testing, emphasizing load testing, stress testing, and scalability testing. It measures the system's behavior under different user loads to ensure optimal performance and identify potential issues. 4. Gatling: Gatling is a tool primarily used for performance testing and load testing. It leverages the Scala programming language to create and execute scenarios, providing detailed reports on system performance and identifying performance bottlenecks. Conclusion Non-functional testing is like a complete health check-up of the software, looking beyond its basic functions. We explored various types of non-functional testing, each with its own purpose. For instance, performance testing ensures our software is fast and efficient, usability testing focuses on making it user-friendly, and security testing protects against cyber threats. Now, why do we need tools for this? Testing tools, like the ones mentioned, act as superheroes for organizations. They help us do these complex tests quickly and accurately. Imagine trying to check how 1,000 people use our app at the same time – it's almost impossible without tools! Various tools simulate real-life situations, find problems and ensure our software is strong and reliable. They save time, money and make sure our software is ready. Related to Integration Testing Frequently Asked Questions 1. What are the types of functional testing? The types of functional testing include unit testing, integration testing, system testing, regression testing, and acceptance testing. 2. How does a smoke test work? Non-functional testing in QA focuses on aspects other than the functionality of the software, such as performance, usability, reliability, security, and scalability. 3. Which are all non-functional testing? The types of non-functional testing include performance testing, load testing, stress testing, usability testing, reliability testing, security testing, compatibility testing, and scalability testing. For your next read Dive deeper with these related posts! 07 Min. Read What is Functional Testing? Types and Examples Learn More 11 Min. Read What is Software Testing? A Complete Guide Learn More Add a Title What is Integration Testing? 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Discover practical TDD strategies used by top engineering teams. Learn what works, what doesn’t, and how to adopt TDD effectively in real-world setups. 12 March 2025 08 Min. Read Test-Driven Development in Modern Engineering WhatsApp LinkedIn X (Twitter) Copy link Automate TDD with HyperTest Ever been in that meeting where the team is arguing about implementing TDD because "it slows us down"? Or maybe you've been the one saying "we don't have time for that" right before spending three days hunting down a regression bug that proper testing would have caught in minutes? I've been there too. As an engineering manager with teams across three continents, I've seen the TDD debate play out countless times. And I've collected the battle scars—and success stories—to share. Let's cut through the theory and talk about what's actually working in the trenches. The Real-World TDD Challenge In 20+ years of software development, I've heard every argument against TDD: "We're moving too fast for tests." "Tests are just extra code to maintain." "Our product is unique and can't be easily tested." Sound familiar? But let me share what happened at Fintech startup Lendify: The team was shipping features at breakneck speed, skipping tests to "save time." Six months later, their velocity had cratered as they struggled with an unstable codebase. One engineer put it perfectly on Reddit: "We spent 80% of our sprint fixing bugs from the last sprint. TDD wasn't slowing us down—NOT doing TDD was." We break down more real-world strategies like this in TDD Monthly , where engineering leaders share what’s working—and what’s not—in their teams. TDD Isn't Theory: It's Risk Management Let's be clear: TDD is risk management. Every line of untested code is technical debt waiting to explode. Metric Traditional Development Test-Driven Development Real-World Impact Development Time Seemingly faster initially Seemingly slower initially "My team at Shopify thought TDD would slow us down. After 3 months, our velocity doubled because we spent less time debugging." - Engineering Director on HackerNews Bug Rate 15-50 bugs per 1,000 lines of code 2-5 bugs per 1,000 lines of code "We reduced customer-reported critical bugs by 87% after adopting TDD for our payment processing module." - Thread on r/ExperiencedDevs Onboarding Time 4-6 weeks for new hires to be productive 2-3 weeks for new hires to be productive "Tests act as living documentation. New engineers can understand what code is supposed to do without having to ask." - Engineering Manager on Twitter Refactoring Risk High - Changes often break existing functionality Low - Tests catch regressions immediately "We completely rewrote our authentication system with zero production incidents because our test coverage gave us confidence." - CTO comment on LinkedIn Technical Debt Accumulates rapidly Accumulates more slowly "Our legacy codebase with no tests takes 5x longer to modify than our new TDD-based services." - Survey response from DevOps Conference Deployment Confidence Low - "Hope it works" High - "Know it works" "We went from monthly to daily releases after implementing TDD across our core services." - Engineering VP at SaaS Conference What Modern TDD really looks like? The problem with most TDD articles is they're written by evangelists who haven't shipped real products on tight deadlines. Here's how engineering teams are actually implementing TDD in 2025: 1. Pragmatic Test Selection Not all code deserves the same level of testing. Leading teams are applying a risk-based approach: High-Risk Components : Payment processing, data storage, security features → 100% TDD coverage Medium-Risk Components : Business logic, API endpoints → 80% TDD coverage Low-Risk Components : UI polish, non-critical features → Minimal testing As one VP Engineering shared on a leadership forum: "We apply TDD where it matters most. For us, that's our transaction engine. We can recover from a UI glitch, but not from corrupted financial data." 2. Inside-Out vs Outside-In: Real Experiences The debate between Inside-Out (Detroit) and Outside-In (London) approaches isn't academic—it's about matching your testing strategy to your product reality. From a lead developer at Twilio on their engineering blog: "Inside-Out TDD worked beautifully for our communications infrastructure where the core logic is complex. But for our dashboard, Outside-In testing caught more real-world issues because it started from the user perspective." 3. TDD and Modern Architecture One Reddit thread from r/softwarearchitecture highlighted an interesting trend: TDD adoption is highest in microservice architectures where services have clear boundaries: "Microservices forced us to define clear contracts between systems. This naturally led to better testing discipline because the integration points were explicit." Many teams report starting with TDD at service boundaries and working inward: Write tests for service API contracts first Mock external dependencies Implement service logic to satisfy the tests Move to integration tests only after unit tests pass Field-Tested TDD Practices That Actually Work Based on discussions with dozens of engineering leaders and documented case studies, here are the practices that are delivering results in production environments: 1. Test-First, But Be Strategic From a Director of Engineering at Atlassian on a dev leadership forum: "We write tests first for core business logic and critical paths. For exploratory UI work, we sometimes code first and backfill tests. The key is being intentional about when to apply pure TDD." 2. Automate Everything The teams seeing the biggest wins from TDD are integrating it into their CI/CD pipelines: Tests run automatically on every commit Pipeline fails fast when tests fail Code coverage reports generated automatically Test metrics tracked over time This is where HyperTest’s approach makes TDD not just practical, but scalable. By auto-generating regression tests directly from real API behavior and diffing changes at the contract level, HyperTest ensures your critical paths are always covered—without needing to manually write every test up front. It integrates into your CI/CD, flags unexpected changes instantly, and gives you the safety net TDD promises, with a fraction of the overhead. 💡 Want more field insights, case studies, and actionable tips on TDD? Check out TDD Monthly , our curated LinkedIn newsletter where we dive deeper into how real teams are evolving their testing practices. 3. Start Small and Scale The most successful TDD implementations didn't try to boil the ocean: Start with a single team or component Measure the impact on quality and velocity Use those metrics to convince skeptics Gradually expand to other teams From an engineering manager at Shopify on their tech blog: "We started with just our checkout service. After three months, bug reports dropped 72%. That gave us the ammunition to roll TDD out to other teams." Overcoming Common TDD Resistance Points Let's address the real barriers engineering teams face when adopting TDD: 1. "We're moving too fast for tests" This is by far the most common objection I hear from startup teams. But interestingly, a CTO study from First Round Capital found that teams practicing TDD were actually shipping 21% faster after 12 months—despite the initial slowdown. 2. "Legacy code is too hard to test" Many teams struggle with applying TDD to existing codebases. The pragmatic approach from engineering leaders who've solved this: Don't boil the ocean : Leave stable legacy code alone Apply the strangler pattern : Write tests for code you're about to change Create seams : Introduce interfaces that make code more testable Write characterization tests : Create tests that document current behavior before changes As one Staff Engineer at Adobe shared on GitHub: "We didn't try to add tests to our entire codebase at once. Instead, we created a 'test firewall'—we required tests for any code that touched our payment processing system. Gradually, we expanded that safety zone." 3. "Our team doesn't know how to write good tests" This is a legitimate concern—poorly written tests can be more burden than benefit. Successful TDD adoptions typically include: Pairing sessions focused on test writing Code reviews specifically for test quality Shared test patterns and anti-patterns documentation Regular test suite health metrics Making TDD Work in Your Organization: A Playbook Based on successful implementations across dozens of engineering organizations, here's a practical playbook for making TDD work in your team: 1. Start with a Pilot Project Choose a component that meets these criteria: High business value Moderate complexity Clear interfaces Active development From an engineering director who led TDD adoption at Adobe: "We started with our license validation service—critical enough that quality mattered, but contained enough that it felt manageable. Within three months, our pilot team became TDD evangelists to the rest of the organization." 2. Invest in Developer Testing Skills The biggest predictor of TDD success? How skilled your developers are at writing tests. Effective approaches include: Dedicated testing workshops (2-3 days) Pair programming sessions focused on test writing Regular test review sessions Internal documentation of test patterns 3. Adapt to Your Context TDD isn't one-size-fits-all. The best implementations adapt to their development context: Context TDD Adaptation Frontend UI Focus on component behavior, not pixel-perfect rendering Data Science Test data transformations and model interfaces Microservices Emphasize contract testing at service boundaries Legacy Systems Apply TDD to new changes, gradually improve test coverage 4. Create Supportive Infrastructure Teams struggling with TDD often lack the right infrastructure: Fast test runners (sub-5 minute test suites) Test environment management Reliable CI integration Consistent mocking/stubbing approaches Clear test data management Stop juggling multiple environments and manually setting up data for every possible scenario. Discover a simpler, more scalable approach here. Conclusion: TDD as a Competitive Advantage Test-Driven Development isn't just an engineering practice—it's a business advantage. Teams that master TDD ship more reliable software, iterate faster over time, and spend less time firefighting. The engineering leaders who've successfully implemented TDD all share a common insight: the initial investment pays dividends throughout the product lifecycle. As one engineering VP at Intercom shared: "We measure the cost of TDD in days, but we measure the benefits in months and years. Every hour spent writing tests saves multiple hours of debugging, customer support, and reputation repair." In an environment where software quality directly impacts business outcomes, TDD isn't a luxury—it's a necessity for teams that want to move fast without breaking things. Looking for TDD insights beyond theory? TDD Monthly curates hard-earned lessons from engineering leaders, every month on LinkedIn. About the Author : As an engineering manager with 15+ years leading software teams across financial services, e-commerce, and healthcare, I've implemented TDD in organizations ranging from early-stage startups to Fortune 500 companies. Connect with me on LinkedIn to continue the conversation about pragmatic software quality practices. Related to Integration Testing Frequently Asked Questions 1. What is Test-Driven Development (TDD) and why is it important? Test-Driven Development (TDD) is a software development approach where tests are written before code. It improves code quality, reduces bugs, and supports faster iterations. 2. How do modern engineering teams implement TDD successfully? Modern teams use a strategic mix of test-first development, automation in CI/CD, and gradual scaling. Tools like HyperTest help automate regression testing and streamline workflows. 3. Is TDD suitable for all types of projects? While TDD is especially effective for backend and API-heavy systems, its principles can be adapted for UI and exploratory work. Teams often apply TDD selectively based on context. For your next read Dive deeper with these related posts! 07 Min. Read Choosing the right monitoring tools: Guide for Tech Teams Learn More 09 Min. Read CI/CD tools showdown: Is Jenkins still the best choice? Learn More 07 Min. Read Optimize DORA Metrics with HyperTest for better delivery Learn More