HyperTest generates integration tests that achieve over 90% coverage, ensuring fast and bug-free deployment of distributed services. AI Reviews Miss Runtime Errors. We Don't. HyperTest uses runtime traces to review code changes. Cut through noise. 5-10 deep findings that break production. High signal. Deep context. Try it now Book a Live Demo WEBINAR | On-Demand | "No More Writing Mocks: The Future of Unit & Integration Testing" >> Why we built HyperTest? Unit tests are useful for checking the logic within a service but fail to test the dependencies between services. Integration testing comes to the rescue, but as opposed to the well-standardized unit testing frameworks, there was no off-the-shelf integration testing framework that we could use for our back-end services. Paul Marinescu Research Scientist View Source How it Works For Developers For Engineering Leaders Enables developers to quickly fix integration issues Manual Mocking is History No more writing & maintaining brittle test mocks Real-World Testing Test based on actual API interactions and edge cases Ship Faster Reduce testing time by 80% with automated verification Why Should Engineering Managers Consider it? Missing Delivery Deadlines Ineffective automated testing # 1 reason for slow releases High Technical Debt Complex codebase that is becoming hard to maintain with high risk for failures and downtimes Low Developer Productivity Developers spending all their time fixing issues risking burnout and no time for innovation Learn how it works 100% Autonomous Record and Replay. Generates integration tests automatically from real user traffic. Fully autonomous with Zero maintenance. 2 min. Setup Add 2-line SDK in your application code. Records tests from any environment to cover >90% lines of code in a few hours. Catch Bugs Early Run tests as automated checks pre-commit or with a PR. Release new changes bug-free in minutes, not days or weeks. Hear from our Customers HyperTest has been a game-changer for us in Integration 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 We have recently upgraded our code framework. And by running one instance of Hypertest, we got the first-cut errors in less than an hour , which could have taken us a few days. Vibhor G VP of Engineering Hypertest unique selling point is its ability to generate tests by capturing network traffic, they have reduced the overhead of writing test cases, and its reports and integrations have helped us smoke out bugs very quickly with very little manual intervention. Ajay Srinivasan Senior Technical Lead Trace failing requests across microservices Test Service Mesh with Distributed Tracing HyperTest context propagation provides traces across multiple microservices, helping developers debug root causes in a single view. It cuts debugging time and tracks data flow between services, showing the entire chain of events leading to failure. Read More Test code, APIs, data, queues without writing tests Power of foundational models with Record and Replay Test workflows, data and schema across APIs, database calls and message queues. Generate tests from real userflows to uncover problems that only appear in production like environments Read More Shift-left with your CI pipeline Release with High Coverage without writing tests Forget writing unit tests and measure all tested and untested parts of your code. Cover legacy to new code in days. Read More Top Use Cases From APIs to Queues, Databases to Microservices: Master Your Integrations High Unit Test Coverage HyperTest can help you achieve high >90% of code coverage autonomously and at scale. We can write 365 days of effort in less than a few hours. Database Integrations It can test the integration between your application and its databases, ensuring data consistency, accuracy, and proper handling of database transactions. API Testing HyperTest can validate the interactions between different components of your application through API testing. It ensures that APIs are functioning correctly and communicate seamlessly. Message Queue Testing If your application relies on message queues for communication, HyperTest can verify the correct sending, receiving, and processing of messages. Microservices Testing HyperTest is designed to handle the complexities of testing microservices, ensuring that these independently deployable services work harmoniously together. 3rd-Party Service Testing It can test the integration with external services and APIs, ensuring that your application can effectively communicate with third-party providers. HyperTest in Numbers 2024 Year 8,547 Test Runs 8 million+ Regressions 100+ Product Teams Prevent Logical bugs in your database calls, queues and external APIs or services Get Started for Free Developers at the most innovative companies trust HyperTest for confident releases

Explore the comprehensive comparison between PACT and HyperTest to understand how they revolutionize API contract testing PACT Comparison Card Aspect Without HyperTest With HyperTest Scope of assertions Units tests only verify the correctness of the code under test Integration style tests that verify correctness of code, API response, inter service contracts, queue messages and database queries Quality of assertions Hand written assertions might cover or miss logical correctness Programmatic assertions that are deeper and wider and cover every corner case Realism of test scenarios Build and test scenarios that devs believe they know Builds test from real-world scenarios and covers application faster than hand written tests PACT style contract tests Manual effort writing contract tests and updating if contract change Automatically generate contract tests and update if they change. Manual effort eliminated. Quality of contract Tests PACT style contract tests will catch change in schema but will miss change in data value HyperTest generated contract tests will catch schema changes as well as change in data value Collaboration If producer changes the contract for the consumer, it needs to update the PACT file for the consumer fails in production If producer updates the contract, consumer is immediately notified before producer merges to production Resilience to Changes Tests may become outdated and less effective as external services change without corresponding updates to test cases. Tests remain relevant and effective over time as Hypertest adapts to changes in external services. Test Maintenance Requires ongoing maintenance of tests to keep pace with changes. No maintenance. Tests and mocks are auto-generated and auto-refreshed. Scope of testing Limited to testing internal logic and behavior of code units. Will miss integration failures like breaking contracts b/w services Covers integration scenarios Prevent Logical bugs in your databases calls, queues and external APIs or services Take a Live Tour Book a Demo

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Katalon Comparison Card Aspect/ Feature Katalon HyperTest What does it do? End to end automation including UI and API Complete backend testing: APIs, database calls, message queues & inter-service contracts Who are its users? Will not test database calls, async flows or message queues. Only Developers What will it not do? SDETs, test and QA engineers Front-end testing: will not test UI or test across browsers or devices How to start? Katalon Studio. Provides a comprehensive IDE for writing, recording, and executing tests 10-line SDK in the source code of repo. Records traffic and builds tests which can be replayed later using CLI How does it work? Recording and Scripting: Users can record their actions on web or mobile applications, generating automated test scripts. They can also write custom scripts using Groovy Record and Replay: Monitors application traffic using its SDK to generate backend tests automatically. 100% autonomous. Where does it run tests? Katalon Cloud. Needs dedicated and test isolated environments with SUT and dependencies available Environment Agnostic. No dedicated or isolated environments needed. These tests can be run locally 100% local or before push-commit in CI Maintenance Manual. Write and update tests using Katalon Studio manually 100% autonomous or no-code. Automatically generates tests by recording actual user flows. Auto updates all test cases and assertions as APIs change Quality of Tests Poor. Depends on quality of assertions which are manually written High. Quality programmatically generated assertions that cover schema & data to never miss errors Test Coverage Unknown. No way to measure test coverage that can result in poor coverage and untested scenarios Measurable. Reports code coverage i.e. actual lines tested lines of code leaving behind no untested scenario Test Data Management Yes. users need to use data sheets or custom scripts to seed and manipulate data for tests. Can turn out to be non-trivial No. HyperTest uses data from traffic for tests and keeps it reusable. Handles both read & write requests Test Execution Speed High. API test: Depends on response time of API and run environment End to end test: Take longer to run Negligible. Runs as fast as unit tests. Does not need dedicated environments but are still able to test e2e backend flows Can it test databases? No Yes Can it test message queues? No Yes Prevent Logical bugs in your database calls, queues and external APIs or services Get Started for Free

Regression Testing is the reevaluation of software functionality after updates to ensure new code aligns with and doesn’t break existing features. 20 February 2024 11 Min. Read What is Regression Testing? Tools, Examples and Techniques WhatsApp LinkedIn X (Twitter) Copy link Download the Checklist What Are the Different Types of Regression Testing? Different types of regression testing exist which cater to varying needs of the software development lifecycle. The choice of regression testing type depends on the scope and impact of changes, allowing testing and development teams to strike a balance between thorough validation and resource efficiency. The following are the types of regression testing. 1.Unit Regression Testing: Isolated and focused testing on individual units of the software. Validates that changes made to a specific unit do not introduce regressions in its functionality. The efficiency of this lies in catching issues within a confined scope without testing the entire system. 2. Partial Regression Testing: This involves testing a part of the entire application and focusing on modules and functionalities that are affected by recent changes. The benefit of partial regression testing is that it saves time and resources especially when the modifications are localised. Balances thorough testing with efficiency by targeting relevant areas impacted by recent updates. 3. Complete Regression Testing: This involves regression testing of the entire application thereby validating all modules and functionalities. It is essential when there are widespread changes that impact the software. It ensures overall coverage even though it is time-consuming when compared to partial regression testing. Regression Testing Techniques Now that we know what the different types of regression testing are, let us focus on the techniques used for the same. Regression testing techniques offer flexibility and adaptability that allow development and testing teams to tailor their approach towards testing based on the nature of changes, project size and resource constraints. Specific techniques are selected depending on the project’s requirements which, in turn, ensures a balance between validation and efficient use of testing resources. The following are the techniques teams use for regression testing: 1.Regression Test Selection: It involves choosing a part of the test cases based on the impacted areas of recent changes. Its main focus is on optimising testing efforts by selecting relevant tests for correct validation. 2. Test Case Prioritization: This means that test cases are ranked based on criticality and likelihood of detecting defects. This maximises efficiency as it tests high-priority cases first thereby allowing the early detection of regressions. 3. Re-test All: This requires that the entire suite of test cases be run after each code modification. This can be time-consuming for large projects but is ultimately an accurate means to ensure comprehensive validation. 4. Hybrid: It combines various regression testing techniques like selective testing and prioritisation to optimise testing efforts. It adapts to the specific needs of the project and thus, strikes a balance between thoroughness and efficiency. 5. Corrective Regression Testing: The focus is on validating the measures applied to resolve the defects that have been identified. This verifies that the added remedies do not create new issues or impact existing functionalities negatively. 6. Progressive Regression Testing: This incorporates progressive testing as changes are made during the development process. This allows for continuous validation and thus minimising the likelihood of accumulating regressions. 7. Selective Regression Testing: Specific test cases are chosen based on the areas affected by recent changes. Testing efforts are streamlined by targeting relevant functionalities in projects with limited resources. 8. Partial Regression Testing: It involves testing only a subset of the entire application. This proves it to be efficient in validating localized changes without the need for the entire system to be retested. 5 Top Regression Testing Tools in 2024 Regression testing is one of the most critical phases in software development, ensuring that modifications to code do not inadvertently introduce defects. Using advanced tools can not only significantly enhance the efficiency of regression testing processes but also the accuracy of the same. We have covered both the free and the paid Regression Testing tools. The top 5 best performing Regression Testing Tools to consider for 2024 are: HyperTest Katalon Postman Selenium testRigor 1. HyperTest - Regression Testing Tool: HyperTest is a regression testing tool that is designed for modern web applications. It offers automated testing capabilities, enabling developers and testers to efficiently validate software changes and identify potential regressions. HyperTest auto-generates integration tests from production traffic, so you don't have to write single test cases to test your service integration. For more on how HyperTest can efficiently take care of your regression testing needs, visit their website here . 👉 Try HyperTest Now 2. Katalon - Regression Testing Tool: Katalon is an automation tool that supports both web and mobile applications. Its simplified interface makes regression testing very easy thereby enabling accessibility for both beginners and experienced testers. Know About - Katalon Alternatives and Competitors 3. Postman - Regression Testing Tool: While renowned for Application Programming Interface (API) testing , Postman also facilitates regression testing through its automation capabilities. It allows testers and developers to create and run automated tests , ensuring the stability of APIs and related functionalities. Know About - Postman Vs HyperTest - Which is More Powerful? 4. Selenium - Regression Testing Tool: Selenium is a widely used open-source tool for web application testing. Its support for various programming languages and browsers makes it a go-to choice for regression testing, providing a scalable solution for diverse projects. 5. testRigor - Regression Testing Tool: testRigor employs artificial intelligence to automate regression testing . It excels in adapting to changes in the application, providing an intelligent and efficient approach to regression testing. Regression Testing With HyperTest Imagine a scenario where a crucial financial calculation API, widely used across various services in a fintech application, receives an update. This update inadvertently changes the data type expectation for a key input parameter from an integer (int) to a floating-point number (float). Such a change, seemingly minor at the implementation level, has far-reaching implications for dependent services that are not designed to handle this new data type expectation. The Breakdown The API in question is essential for calculating user rewards based on their transaction amounts. ➡️Previously, the API expected transaction amounts to be sent as integers (e.g., 100 for $1.00, considering a simplified scenario where the smallest currency unit is integrated into the amount, avoiding the need for floating-point arithmetic). ➡️However, after the update, it starts expecting these amounts in a floating-point format to accommodate more precise calculations (e.g., 1.00 for $1.00). ➡️Dependent services, unaware of this change, continue to send transaction amounts as integers. The API, now expecting floats, misinterprets these integers, leading to incorrect reward calculations. ➡️ Some services might even fail to call the API successfully due to strict type checking, causing transaction processes to fail, which in turn leads to user frustration and trust issues. ➡️As these errors propagate, the application experiences increased failure rates, ultimately crashing due to the overwhelming number of incorrect data handling exceptions. This not only disrupts the service but also tarnishes the application's reputation due to the apparent unreliability and financial inaccuracies. The Role of HyperTest in Preventing Regression Bugs HyperTest , with its advanced regression testing capabilities, is designed to catch such regressions before they manifest as bugs or errors in the production environment, thus preventing potential downtime or crashes. Here's how HyperTest could prevent the scenario from unfolding: Automated Regression Testing : HyperTest would automatically run a comprehensive suite of regression tests as soon as the API update is deployed in a testing or staging environment. These tests include verifying the data types of inputs and outputs to ensure they match expected specifications. Data Type Validation : Specifically, HyperTest would have test cases that validate the type of data the API accepts. When the update changes the expected data type from int to float, HyperTest would flag this as a potential regression issue because the dependent services' test cases would fail, indicating they are sending integers instead of floats. Immediate Feedback : Developers receive immediate feedback on the regression issue, highlighting the discrepancy between expected and actual data types. This enables a quick rollback or modification of the dependent services to accommodate the new data type requirement before any changes are deployed to production. Continuous Integration and Deployment (CI/CD) Integration : Integrated into the CI/CD pipeline , HyperTest ensures that this validation happens automatically with every build. This integration means that no update goes into production without passing all regression tests, including those for data type compatibility. Comprehensive Coverage : HyperTest provides comprehensive test coverage, ensuring that all aspects of the API and dependent services are tested, including data types, response codes, and business logic. This thorough approach catches issues that might not be immediately obvious, such as the downstream effects of a minor data type change. By leveraging HyperTest's capabilities, the fintech application avoids the cascading failures that could lead to a crash and reputational damage. Instead of reacting to issues post-deployment, the development team proactively addresses potential problems, ensuring that updates enhance the application without introducing new risks. HyperTest thus plays a crucial role in maintaining software quality, reliability, and user trust, proving that effective regression testing is indispensable in modern software development workflows. 💡 Schedule a demo here  to learn about this approach better Conclusion We now know how important regression testing is to software development and the stability required for applications during modifications. The various tools employed ensure that software is constantly being tested to detect unintended side effects thus safeguarding against existing functionalities being compromised. The examples of regression testing scenarios highlight why regression testing is so important and at the same time, versatile! Embracing these practices and tools contributes to the overall success of the development lifecycle, ensuring the delivery of high-quality and resilient software products. If teams can follow best practices the correct way, there is no stopping what regression testing can achieve for the industry. Please do visit HyperTest to learn more about the same. Frequently Asked Questions 1. What is regression testing with examples? Regression testing ensures new changes don't break existing functionality. Example: Testing after software updates. 2. Which tool is used for regression? Tools: HyperTest, Katalon, Postman, Selenium, testRigor 3. Why is it called regression testing? It's called "regression testing" to ensure no "regression" or setbacks occur in previously working features. For your next read Dive deeper with these related posts! 07 Min. Read FinTech Regression Testing Essentials Learn More 08 Min. Read What is API Test Automation?: Tools and Best Practices Learn More 07 Min. 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Explore the pros and cons of end-to-end testing. Gain insights into its benefits for software reliability and the challenges it may pose in development. 6 February 2024 09 Min. Read The Pros and Cons of End-to-End Testing WhatsApp LinkedIn X (Twitter) Copy link Get Tailored Approach Let's talk about end-to-end testing – it's like the superhero at the top of Mike Cohn's testing pyramid! These tests are the final line of defense, and even though there aren't many of them, they're like a super-strong shield against sneaky bugs trying to get into the production party. But, here's the million-dollar question: do they really live up to all the hype? Well, that's why we're here with this blog! We're going to talk about the pros and cons of end-to-end testing . Plus, we’ve an alternative approach to discuss yielding out the same or even better results, without you having to write any test-scripts for that. Let's dive into the world of E2E testing and find out what's really going on! What is End-to-End testing? So let’s get the basics clear, we start with simply explaining what is E2E tests, so that we don’t have any confusions as we go ahead with the blog. E2E are the high-level tests that are performed at the end of the testing phase. The focus is to test individual components together as a work-flow from a user’s perspective. While unit tests focuses on testing those individual components in isolation, E2E combines them together as a single working unit and run a test on that. End-to-end testing is a methodology used to verify the completeness and correctness of a software application from start to finish. The main goal of E2E testing is to simulate real user scenarios to ensure the system behaves as expected in a fully integrated environment. All the dependent services, third-party integrations, databases need to be kept up and running, mimicking the real scenario with all possible dependencies. It helps in evaluating the system's external interfaces and ensures all integrated components work together seamlessly to carry out any task a user might perform. Key Features of E2E Testing: Comprehensive Coverage: Tests the application's workflow from beginning to end. Real User Simulation: Mimics real user behaviors and interactions with the application. Integration Verification: Ensures that all parts of the system work together correctly. Environment Validation: Confirms that the application works as expected in environments that mimic production settings. Types/ Strategies of E2E testing End-to-end (E2E) testing strategies are essential for ensuring that software systems meet their designed functions and user expectations comprehensively. Among these strategies, horizontal and vertical E2E testing stand out for their unique approaches and focuses. While both aim to validate the complete functionality of a system, their methodologies and perspectives differ significantly. 1. Horizontal E2E Testing Horizontal E2E testing examines the system's workflow as it would occur in its operational environment, emphasizing a wide-ranging approach that covers the system's full spectrum of functionalities. This method aligns closely with the user's perspective, traversing through the application's various interfaces and interactions just as an end user would. Characteristics: It simulates real-world user scenarios, navigating through the application's user interface (UI), engaging with different features, and integrating with external systems, if applicable. The objective is to replicate the typical user journey as closely as possible. In an online booking system, horizontal testing would involve steps like searching for a service, selecting an option, entering user details, proceeding through payment, and receiving a confirmation—all through the UI. 2. Vertical E2E Testing Contrastingly, vertical E2E testing delves into the system's architecture, examining the integration and data flow between layers or components from a more technical standpoint. It is particularly effective in early development stages or for complex systems where layer-specific functionality needs thorough validation. This approach tests the system's internal processes, from the database layer through business logic and up to the presentation layer, in a sequential manner. It's highly focused on backend operations, data integrity, and the integration between different system components. For a cloud storage service, vertical testing might verify the process of uploading a file, ensuring that the file passes correctly from the front end, through the application logic, is stored properly in the database, and is accessible for future retrieval. How to perform E2E tests for an Online E-Commerce Store? Objective: To conduct a thorough End-to-End (E2E) testing on an online shopping platform to ensure a seamless shopping experience from account registration to order confirmation. Test Strategy Development: Aim: To validate the complete functionality of the e-commerce platform, ensuring that all user actions lead to the expected outcomes without errors. Key Customer Journey to Test: The process from creating a new account, finding products, adding items to the cart, checking out, making payment, to receiving an order confirmation. Testing Environment Configuration: Set up a staging environment that closely mirrors the production environment, including web servers, databases, and mock services for external integrations like payment gateways. Test Cases Development: Account Registration Purpose: To confirm that users can successfully register on the platform. Procedure: Navigate to the signup page, fill out the registration form with details (username, email, password), and submit. Expected Result: The user is registered and receives a confirmation email. Login Functionality Purpose: To ensure that the login mechanism works correctly with valid user credentials. Procedure: Go to the login page, enter valid email and password, and submit. Expected Result: The user is logged into their account and directed to the homepage. Product Browsing and Selection Purpose: To verify that users can browse through the product listings and access product details. Procedure: Visit the product listing section, choose a category, and select a product to view its details. Expected Result: The product's details page loads with all the relevant information. Adding Product to Cart Purpose: To test the functionality of adding products to the shopping cart. Procedure: From a product's details page, click the "Add to Cart" button. Expected Result: The product is added to the cart, and the cart's item count is updated. Checkout Process Purpose: To confirm the checkout process is intuitive and error-free. Procedure: Access the shopping cart, click "Proceed to Checkout," enter necessary shipping and billing information, and submit. Expected Result: The user is navigated to the payment page. Payment Transaction Purpose: To ensure the payment process is secure and processes transactions correctly using mock payment details. Procedure: Input mock payment information and submit. Expected Result: The payment is processed, and an order confirmation screen is shown. Order Confirmation Purpose: To verify that the order confirmation details are accurate and an email confirmation is sent. Procedure: After payment, confirm the details on the order confirmation page and check for an email confirmation. Expected Result: The order details are correct, and an email confirmation is received. Preparation of Test Data: Data Needed: User credentials for login tests, product details for browsing and selection, and mock payment information for checkout. Perform End-to-end testing without the need to prepare test data, learn how? Execution of Tests: Automated testing scripts (e.g., using Selenium WebDriver) will simulate the user journey from registration to order confirmation, asserting the expected outcomes at each step. # Example of a Python script using Selenium for automated E2E testing from selenium import webdriver import unittest class E2ETesting(unittest.TestCase): def setUp(self): self.browser = webdriver.Chrome('path/to/chromedriver') self.addCleanup(self.browser.quit) def testCompleteUserJourney(self): # Detailed steps for each part of the test go here, including: # - Navigating to the site # - Registering a new account # - Logging in # - Browsing products and adding to cart # - Checking out # - Verifying order confirmation pass if __name__ == '__main__': unittest.main() Analysis of Test Results: After executing tests, analyze logs and outputs to identify any bugs or issues with the platform. Test Reporting: Compile a detailed report of the testing process, findings, and recommendations. This report should include test coverage details, success rates, bugs identified, and screenshots or logs as evidence. This comprehensive approach to E2E testing ensures the online shopping platform functions correctly across all user interactions, offering stakeholders confidence in the platform's reliability and user satisfaction. The Pros of E2E testing E2E tests offers the full picture of the test scenario, offering advantages like: Replicates Real-User Experience : E2E testing evaluates the system's overall functionality and its interaction with external interfaces, databases, and other systems, mirroring real-world user scenarios and behaviors. Scenario: Testing a login feature in an application. describe('Login Feature', () => { it('successfully logs in the user', () => { cy.visit('/login') // Navigate to the login page .get('input[name="email"]').type('user@example.com') // Enter email .get('input[name="password"]').type('password123') // Enter password .get('form').submit() // Submit the login form .get('.welcome-message').should('contain', 'Welcome back, user!'); // Verify login success }); }); Real-User Experience: This code simulates a user navigating to the login page, entering their credentials, and submitting the form, closely mirroring a real user's actions. Increases Confidence: Verifying the presence of a welcome message after login actions ensures the application behaves as expected, boosting confidence in deployment. 2. Identifies System-wide Issues : It helps uncover issues related to data integrity, services integration, and user interface, which might not be detected during unit or integration testing phases. 3. Facilitates Compliance with Requirements : For applications in regulated sectors, E2E testing ensures that the software meets necessary compliance standards, including security protocols and data handling practices. 4. Supports Continuous Integration/Continuous Deployment (CI/CD) : Automated E2E tests can be integrated into CI/CD pipelines, enabling regular testing at various stages of development, which helps in identifying and addressing issues promptly. The Cons of E2E testing This test pyramid approach needs to be modified for testing microservices. E2E tests need to be completely dropped. Apart from taking a long time to build and maintain, E2E tests execute complete user-flows every time on the entire application, with every test. This requires all services under the hood to be simultaneously brought up (including upstream) even when it is possible to catch the same kind and the same number of failures by testing only a selected group of services; only the ones that have undergone a change. Resource Intensive : E2E testing can be time-consuming and expensive due to the need for comprehensive test cases, the setup of testing environments that mimic production, and potentially longer execution times for tests. Scenario: Setting up a Selenium test environment for the same login feature. from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.by import By # Setup WebDriver driver = webdriver.Chrome() # Navigate to the login page driver.get("http://example.com/login") # Enter login details and submit driver.find_element(By.NAME, "email").send_keys("user@example.com") driver.find_element(By.NAME, "password").send_keys("password123") driver.find_element(By.NAME, "submit").click() # Verification assert "Welcome back, user!" in driver.page_source # Teardown driver.close() Resource Intensiveness: Setting up Selenium, managing WebDriver instances, and ensuring the environment matches the production settings can be time-consuming and resource-heavy. Complexity in Maintenance: The Selenium example requires explicit browser management (setup and teardown), which adds to the complexity, especially when scaling across different browsers and environments. Flakiness and Reliability Issues : E2E tests can sometimes produce inconsistent results due to their reliance on multiple external systems and networks, leading to flakiness in test outcomes. Slow Feedback Loop : Due to the extensive nature of E2E tests, there can be a significant delay in getting feedback, which can slow down the development process, particularly in agile environments that prioritize quick iterations. Not Suited for All Types of Testing : E2E testing is not always the best choice for detecting specific, low-level code issues, which are better identified through unit testing or integration testing . Perform E2E Testing without test data preparation The flakiness and complexity of End-to-End (E2E) tasks often stem from the need for test data preparation. For E2E scenarios to run smoothly, it's essential to create and maintain relevant test data. In the context of app testing, particularly for e-commerce platforms like Nykaa or Flipkart, the process is akin to testing different states of the app. For example, verifying if a user can apply loyalty points for a discount involves specific state testing. Requirements for Test Data: To test the aforementioned scenario, a QA engineer must prepare several pieces of test data, including: A valid user account A valid product listing Sufficient inventory for the product The addition of the product to a shopping cart This setup is necessary before the app reaches the state where the discount via loyalty points can be applied. The scenario described is relatively straightforward. However, an e-commerce app may contain hundreds of such flows requiring test data preparation. Managing the test data and app states for numerous scenarios significantly increases the workload and stress for QA engineers. Fortunately, there exists a straightforward approach that allows QA engineers to test the functionality of an application without the need for extensive test data creation and management. This method focuses on testing the core functions directly, alleviating the burden of test data preparation. Click here to learn more now . Conclusion Concluding our discussion on the pros and cons of end-to-end (E2E) testing, it's evident that E2E testing is a critical tool in the software development but it comes at the cost of time, money and effort. They’re extremely difficult to write, maintain and update. An E2E test that actually invokes the inter service communication like a real user would catch this issue. But cost of catching this issue with a test that could involve many services would be very high, given the time and effort spent creating it. imprecise because they've such a broad scope needs the entire system up & running, making it slower and difficult to identify the error initiation point The essence of navigating E2E testing successfully is choosing the right tools , automating where possible, and continuously refining testing processes to align with project needs and goals. Get in touch with us if you want to test E2E scenario’s without needing to spend any time creating and managing test data. Related to Integration Testing Frequently Asked Questions 1. What is E2E testing? End-to-End (E2E) testing ensures seamless software functionality by examining the entire system's components, identifying potential issues, and verifying their integration. 2. What is an example of a bottleneck in performance testing? E2E testing is vital for detecting and preventing integration issues in software development, ensuring a smooth user experience and system reliability. 3. What are the benefits of end-to-end testing? Benefits include early bug detection, improved system reliability, and confidence that the software meets user requirements by validating its entire functionality. For your next read Dive deeper with these related posts! 09 Min. Read Difference Between End To End Testing vs Regression Testing Learn More 07 Min. Read Frontend Testing vs Backend Testing: Key Differences Learn More Add a Title What is Integration Testing? A complete guide Learn More

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Isolate unit tests with Mockito mocks! Learn to mock behavior, explore spies & static methods, and write optimized tests. 21 June 2024 07 Min. Read Mockito Mocks: A Comprehensive Guide WhatsApp LinkedIn X (Twitter) Copy link Get a Demo 💡 Mockito is unreadable for a beginner. So I'm just starting with mockito on Java, and god, it's horrible to read. I mean, reading tests in general requires some practice, but when you get there is like documentation on class methods. Is wonderful. Mockito test, on the other hand, are chaotic. -a mockito user on Reddit Well, that’s not a good review for such a famous mocking framework. People have their reasons to have varied opinions, but this guide is our attempt to make mockito sorted for you all. So what is mockito all about? Unit testing – the cornerstone of building reliable, maintainable software. But unit testing can get tricky when you have complex dependencies. That's where Mockito mocks come in, like a superhero for isolated unit tests. Mockito is one of the most popular and powerful mocking frameworks used in Java unit testing. It simplifies the creation of test doubles, or "mocks", which mimic the behavior of complex, real objects in a controlled way, allowing developers to focus on the behavior being tested without setting up elaborate real object environments. Mockito allows testing a method without needing the methods that the method depends on. Introduction to Mocking Mocking is a technique used in unit testing where real implementation details are replaced with simulated behaviors. Mock objects return predetermined responses to method calls, ensuring that the test environment is both controlled and predictable. This is crucial in testing the interactions between components without relying on external dependencies. ⏩Mocks Imagine a mock object as a spy. It pretends to be a real object your code interacts with, but you control its behavior entirely. This lets you test your code's logic in isolation, without worrying about external factors. Why Mockito? Mockito’s ease of use and large community-base is great, but there are other reasons also on why it’s a favored choice among Java devs: Flexibility: It allows testing in isolation and provides numerous ways to tailor mock behavior. Readability: Mockito's syntax is considered clear and concise, making your tests easier to understand and maintain. Versatility: It supports mocking both interfaces and classes, offering flexibility in your testing approach. On the technical front, it offers customizations up to the level of fine-tuning the details in your verifications, keeping tests focused on what matters. Also: Spies: Mockito allows creating spies, which are a type of mock that also record how they were interacted with during the test. Annotations: Mockito provides annotations like @Mock and @InjectMocks for streamlined mock creation and injection, reducing boilerplate code. PowerMock: Mockito integrates with PowerMock, an extension that enables mocking static methods and final classes, giving you more control in complex scenarios. While other frameworks like EasyMock or JMockit may have their strengths, Mockito's overall ease of use, clear syntax, and extensive features make it a preferred choice for many Java developers. Getting Started with Mockito Before right away starting the tech-dive with mockito, let’s first understand some basic jargon terms that comes along with Mockito. Understanding the Jargon first: Mocking: In Mockito, mocking refers to creating a simulated object that imitates the behavior of a real object you depend on in your code. This allows you to isolate and test your code's functionality without relying on external factors or complex dependencies. Mock Object: A mock object is the fake implementation you create using Mockito. It can be a mock for an interface or a class. You define how the mock object responds when methods are called on it during your tests. Stub: While similar to a mock object, a stub is a simpler version. It often provides pre-programmed responses to specific method calls and doesn't offer the same level of flexibility as a full-fledged mock object. Verification: Mockito allows you to verify interactions with your mock objects. This means checking if a specific method on a mock object was called with certain arguments a particular number of times during your test. Verification helps ensure your code interacts with the mock object as expected. @Mock: This annotation instructs Mockito to create a mock object for the specified class or interface. @InjectMocks: This annotation simplifies dependency injection. It tells Mockito to inject the mock objects created with @Mock into the fields annotated with @InjectMocks in your test class Mockito.when(): This method is used to define the behavior of your mock objects. You specify the method call on the mock object and the value it should return or the action it should perform when that method is invoked. Mockito.verify(): This method is used for verification. You specify the method call you want to verify on a mock object and optionally, the number of times it should have been called. Now it’s time to see Mockito in practice Alright, picture a FinTech app. It has two important services: AccountService: This service retrieves information about your account, like the account number. TransactionService: This service handles transactions, like processing a payment. We'll be using Mockito to mock these services so we can test our main application logic without relying on actual accounts or transactions (safer for our virtual wallet!). Step 1: Gearing Up (Adding Mockito) First, we need to include the Mockito library in our project. This is like getting the deck of cards (Mockito) for our testing house of cards. You'll use a tool like Maven or Gradle to manage dependencies, but don't worry about the specifics for now. Step 2: Mocking the Services (Creating Fake Cards) Now, let's create mock objects for our AccountService and TransactionService . We'll use special annotations provided by Mockito to do this: @Mock private AccountService accountService; @Mock private TransactionService transactionService; // More code will come here... @Mock : This annotation tells Mockito to create fake versions of AccountService and TransactionService for us to play with in our tests. Step 3: Putting it all Together (Building the Test) We'll create a test class to see how our FinTech app behaves. Here's a breakdown of what goes inside: @RunWith(MockitoJUnitRunner.class) public class MyFinTechAppTest { @InjectMocks private MyFinTechApp finTechApp; @Before public void setUp() { // This line is important! MockitoAnnotations.initMocks(this); } // Our test cases will go here... } @RunWith(MockitoJUnitRunner.class) : This line tells JUnit (the testing framework) to use Mockito's test runner. Think of it as the table where we'll build our house of cards. @InjectMocks : This injects our mock objects ( accountService and transactionService ) into our finTechApp instance. It's like shuffling the deck (our mocks) and placing them conveniently next to our app (finTechApp) for the test. @Before : This ensures that Mockito properly initializes our mocks before each test case runs. It's like making sure we have a clean deck before each round of playing cards. Step 4: Test Case 1 - Valid Transaction (Building a Successful House of Cards) Let's create a test scenario where a transaction is successful. Here's how we'd set it up: @Test public void testProcessTransaction_Valid() { // What should the mock AccountService return? Mockito.when(accountService.getAccountNumber()).thenReturn("1234567890"); // What should the mock TransactionService do? Mockito.when(transactionService.processTransaction(1000.00, "1234567890")).thenReturn(true); // Call the method in our app that processes the transaction boolean result = finTechApp.processTransaction(1000.0) Advanced Features Spy While mocks return predefined outputs, spies wrap real objects, optionally overriding some methods while keeping the original behavior of others: List list = new ArrayList(); List spyList = Mockito.spy(list); // Use spy object as you would with a mock. when(spyList.size()).thenReturn(100); Capturing Arguments For verifying parameters passed to mock methods, Mockito provides ArgumentCaptor : ArgumentCaptor captor = ArgumentCaptor.forClass(Integer.class); verify(mockedList).get(captor.capture()); assertEquals(Integer.valueOf(0), captor.getValue()); A better approach to Mockito Mocks Mocks generated by mockito are useful, considering the isolation it provides. But the same work can be eased out and performed better by HyperTest. HyperTest mocks external components and auto-refreshes mocks when dependencies change behavior. It smartly mocks external systems like databases, queues, downstream or 3rd party APIs that your code interacts with. It also smartly auto-refreshes these mocks as dependencies change their behavior keeping tests non-flaky, deterministic, trustworthy and consistent. Know more about this approach here in our exclusive whitepaper. Conclusion Mockito mocks offer a robust framework for effectively isolating unit tests from external dependencies and ensuring that components interact correctly. By understanding and utilizing the various features of Mockito, developers can write cleaner, more maintainable, and reliable tests, enhancing the overall quality of software projects. To know more about the automated mock generation process of HyperTest , read it here . Related to Integration Testing Frequently Asked Questions 1. What is the difference between a mock and a spy in Mockito? Mocks are completely fake objects, while spies are real objects wrapped by Mockito. Mocks let you define all behavior, spies keep real behavior but allow customizing specific methods. 2. Can Mockito mock static methods? Yes, Mockito can mock static methods since version 3.4. You use Mockito.mockStatic() to create a scoped mock for the static class. 3. How do you create a mock object in Mockito? Use Mockito.mock(ClassToMock.class) to create a mock object. This replaces a real object with a fake one you control in your test. For your next read Dive deeper with these related posts! 05 Min. Read What is Mockito Mocks: Best Practices and Examples Learn More 10 Min. Read What is Unit testing? A Complete Step By Step Guide Learn More 09 Min. Read Most Popular Unit Testing Tools in 2025 Learn More

Discover why engineering leaders are switching to gRPC—faster communication, lower latency, and better efficiency for modern microservices. 24 February 2025 08 Min. Read gRPC Protocol: Why Engineering Leaders are making the switch? WhatsApp LinkedIn X (Twitter) Copy link Simplify gRPC Testing with HyperTest The efficiency and performance of microservices communication have become crucial in today's fast-changing world. This shift is highlighted by the increasing use of gRPC, a high-performance, open-source universal RPC framework created by Google. As of 2023, major companies like Netflix, Cisco, and Square are reporting large-scale implementations of gRPC, indicating a significant move towards this technology. This article examines why engineering leaders are opting for gRPC over other protocols such as REST or SOAP. Let’s explore this further: What is gRPC? gRPC is a contemporary, open-source, high-performance Remote Procedure Call (RPC) framework that operates in any environment. It defaults to using protocol buffers as its interface definition language (IDL) and message interchange format, providing a compact binary message format that ensures efficient, low-latency communication. gRPC is built to function smoothly across various programming languages, offering a robust method for creating scalable, high-performance services that accommodate streaming and complex multiplexing scenarios. ➡️ How gRPC emerged among other protocols? The development of gRPC was driven by the shortcomings of earlier communication protocols like SOAP and REST, especially within modern, distributed, and microservices-based architectures. Traditional protocols faced challenges with inefficiencies due to bulky data formats and high latency, and they often lacked strong support for real-time communication. A leading e-commerce platform encountered significant challenges with RESTful APIs, including high latency and scalability issues as it expanded. Transitioning to gRPC, which utilizes HTTP/2’s multiplexing, cut latency by as much as 70% and streamlined backend management, greatly improving user experience during peak traffic times. Feature SOAP REST gRPC Transport HTTP, SMTP, TCP HTTP HTTP/2 Data Format XML JSON, XML Protocol Buffers (binary) Performance Lower due to XML verbosity Moderate, depends on data format High, optimized by HTTP/2 and binary data Human Readability Low (XML) High (JSON) Low (binary) Streaming Not supported Not supported Full bidirectional streaming Language Support Extensive via WSDL Language agnostic Extensive, with code generation Security Comprehensive (WS-Security) Basic (SSL/TLS, OAuth) Strong (TLS, ALTS, custom interceptors) Use Case Enterprise, transactional systems Web APIs, public interfaces High-performance microservices Why are Engineers making the switch? ✅ Performance and Efficiency A key reason engineering leaders are shifting to gRPC is its outstanding performance capabilities. By utilizing HTTP/2 as its transport protocol, gRPC enables multiplexing of multiple requests over a single connection, which helps to minimize overhead and latency. Compared to HTTP/1.1, which is used by traditional REST APIs, HTTP/2 can manage a higher volume of messages with a smaller footprint. This is especially advantageous in microservices architectures where services often need to communicate with one another. syntax = "proto3"; package example; // The greeting service definition. service Greeter { // Sends a greeting rpc SayHello (HelloRequest) returns (HelloResponse); } // The request message containing the user's name. message HelloRequest { string name = 1; } // The response message containing the greetings message HelloResponse { string message = 1; } In this straightforward gRPC service example, the ' SayHello' RPC call illustrates how services interact through clearly defined request and response messages, resulting in more predictable and efficient processing. ✅ Scalability Another major benefit of gRPC is its built-in support for bi-directional streaming. This feature allows both the server and client to send a series of messages to each other at the same time, a capability that is not natively available in HTTP/1.1. This is particularly useful for real-time applications like live updates and streaming services. A benchmark study conducted by a leading cloud provider found that gRPC can achieve up to 7 times greater message throughput compared to REST when managing streaming requests and responses. ✅ Language Agnosticism gRPC is compatible with a wide range of programming languages, offering automatic code generation for languages such as Java, C#, Go, Python, and Ruby. This flexibility allows engineering teams to work in their preferred languages while ensuring seamless interoperability through strongly typed interfaces. ✅ Security Security remains a top priority for engineering leaders, and gRPC addresses this concern with strong authentication and encryption features. It supports both Transport Layer Security (TLS) and Application Layer Transport Security (ALTS) for secure communication between clients and servers. Additionally, gRPC services can integrate with middleware to manage authentication, monitoring, and logging, providing an extra layer of security. Netflix has integrated gRPC into several of its systems to leverage its scalability and performance advantages, essential for managing millions of concurrent streams. Similarly, Square has adopted gRPC within its payment systems to ensure reliable and efficient communication among its internal microservices, thereby speeding up transaction processing. Challenges and Considerations While gRPC offers many advantages, it also presents certain challenges. The binary protocol and strict contract definitions can make the initial learning curve steeper and debugging more complex. Additionally, because it uses a binary format, it is less human-readable than JSON, which can complicate API testing and troubleshooting. ➡️ Challenges in Testing gRPC Protocols Testing gRPC protocols comes with unique challenges due to their binary format and strict service contracts. Unlike JSON, which is easy for humans to read and is commonly used in REST APIs, gRPC relies on Protocol Buffers for serializing structured data. While this method is efficient, it can be difficult for humans to interpret, complicating both API testing and troubleshooting in several ways: Dynamic Mocks and Dependencies: Reducing the need to constantly update mocks to keep pace with changing service contracts. Strict Contract Definitions: Making sure that gRPC service definitions in '.proto' files are followed precisely, as any deviations can lead to failures that require careful validation. Error Propagation: Helping to understand and debug gRPC-specific errors, which are different from standard HTTP status codes and necessitate familiarity with a distinct set of error codes. Environment Setup: Simplifying the configuration of test environments for gRPC, which can be challenging and intricate due to the need to replicate real-world scenarios involving multiple services and data flows. Inter-Service Communication: Easing the testing of complex interactions among various services. Identifying Impacted Services: Making it easier to determine which services are affected by code changes in a large microservices architecture. ➡️ How HyperTest Can Assist in Testing gRPC Protocols? HyperTest can significantly streamline and enhance the testing of gRPC protocols by addressing the specific challenges posed by gRPC’s architecture and operation. Here’s how HyperTest can help: Automated Test Generation: HyperTest can automatically generate test cases based on the '.proto' files that define gRPC services. This automation helps ensure that all functions are covered and adhere to the contract specified, reducing human error and oversight. Error Simulation and Analysis: HyperTest records real network traffic and automatically generates tests based on actual user activity. This allows teams to replay and analyze gRPC error codes and network conditions exactly as they occur in production, helping to identify and address potential resilience and error-handling issues before deployment. Continuous Integration (CI) Compatibility: HyperTest integrates seamlessly into CI pipelines, allowing for continuous testing of gRPC services. Compares code changes between your PR and main. Runs only the tests impacted by those changes. Result: CI pipelines that finish in minutes, not hours. Environment Mocking: HyperTest can mock external services and APIs, reducing the necessity for complex environment setups. This feature is particularly useful for microservices architectures where different services may depend on specific responses from other services to function correctly. By leveraging HyperTest, organizations can effectively manage the complexities of testing gRPC services, ensuring robust, reliable, and efficient communication across their distributed systems. This testing framework helps maintain high standards of quality while reducing the overhead and technical challenges associated with manual testing methods. Conclusion gRPC is more than just a new way to make remote calls—it's a powerful paradigm shift for building modern, scalable, and efficient systems. Its benefits span high-performance communication, strong typing, real-time streaming, and seamless scalability. For engineering leaders, this means more robust, reliable, and future-proof architectures. gRPC isn’t going away. But the complexity of testing it shouldn’t hold back your velocity. With HyperTest, you get: ✅ Zero-effort mocks ✅ Pre-deployment dependency impact analysis ✅ CI-optimized test execution Book a Demo to see how teams like yours are deploying gRPC services with confidence. P.S. Still writing mocks by hand? Let’s talk. Related to Integration Testing Frequently Asked Questions 1. Why are companies switching from REST to gRPC? gRPC offers faster performance, lower latency, and efficient binary serialization, making it ideal for microservices. 2. How does gRPC improve scalability in distributed systems? gRPC supports multiplexed streaming and efficient payload handling, reducing overhead and improving performance. 3. How does HyperTest make gRPC testing easier? HyperTest automates contract validation, ensures backward compatibility, and provides real-time distributed tracing for gRPC APIs. 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 RabbitMQ vs. Kafka: When to use what and why? Learn More 09 Min. Read What are stacked diffs and how do they work? Learn More

Testing microservices can be daunting due to their size and complexity. Dive into the intricacies of microservices testing challenges in this comprehensive guide. 19 December 2023 08 Min. Read Microservices Testing Challenges: Ways to Overcome WhatsApp LinkedIn X (Twitter) Copy link Get a Demo What Is Microservices Testing? Microservices architecture is a software design approach where the application is broken down into smaller, independent services that can communicate with each other through APIs. Each service is designed to perform a specific business function and can be developed and deployed independently. In recent years, the trend of adopting microservices architecture has been increasing among organizations. This approach allows developers to build and deploy applications more quickly, enhance scalability, and promote flexibility. Microservices testing is a crucial aspect of ensuring the reliability, functionality, and performance of microservices-based applications. Testing these individual microservices and their interactions is essential to guarantee the overall success of the application. What Is Microservices Testing complex? Switching to this multi-repo system is a clear investment in agility . However, testing microservices can pose significant challenges due to the complexity of the system. Since each service has its own data storage and deployment, it creates more independent elements, which causes multiple points of failure. From complexity and inter-service dependencies to limited testing tools, the microservices landscape can be complex and daunting. Teams must test microservices individually and together to determine their stability and quality. In the absence of a good testing plan, you won't be able to get the most out of microservices. Moreover, you’ll end up regretting your decision to make the switch from monolith to microservice. Implementing micro-services the right way is a lot of hard work, and testing adds to that challenge because of their sheer size and complexity. Let’s understand from Uber's perspective the challenges they had with testing their microservices architecture. Key Challenges in Microservices Testing When you make the switch from a monolithic design to a microservices-based design, you are setting up multiple points of failure. Those failure points become difficult to identify and fix in such an intricately dependent infrastructure. As an application grows in size, the dependency, communication, and coordination between different individual services also increase, adding to the overall complexity of the design. The greater the number of such connections, the more difficult it becomes to prevent failure. According to a DevOps survey, testing microservices is a challenge for 72% of engineering teams. Inter-service Dependency Each individual service is dependent on another for its proper functioning. The more services there are, the higher the number of inter-service communications that might fail. In this complex web of inter-service communications, a breakdown in any of the services has a cascading effect on all others dependent on it. Calls between services can go through many layers, making it hard to understand how they depend on each other. If the nth dependency has a latency spike, it can cause a chain of problems further upstream. Consider a retail e-commerce application composed of microservices like user authentication, product catalog, shopping cart, and payment processing. If the product catalog service is updated or fails, it can affect the shopping cart and payment services, leading to a cascading failure. Testing must account for these dependencies and the ripple effect of changes. Data Management Managing data in a microservices architecture can be a complex task. With services operating independently, data may be stored in various databases, data lakes, or data warehouses. Managing data consistency across services can be challenging, and errors can occur, which can cause significant problems. Customer data may be stored in several databases, and ensuring data consistency can be challenging. For example, if a customer updates their details, the change must reflect in all databases. Ensuring data consistency across different microservices, which might use different databases, is challenging. Testing must cover scenarios where data needs to be synchronized or rolled back across services. An e-commerce application uses separate microservices for order processing and inventory management. Tests must ensure that when an order is placed, the inventory is updated consistently, even if one of the services temporarily fails. class OrderService: def process_order(order_id, product_id, quantity): # Process the order try: InventoryService.update_inventory(product_id, -quantity) Database.commit() # Commit both order processing and inventory update except InventoryUpdateFailure: Database.rollback() # Rollback the transaction in case of failure raise OrderProcessingFailure("Failed to process order due to inventory issue.") class InventoryService: def update_inventory(product_id, quantity_change): # Update the inventory if not InventoryDatabase.has_enough_stock(product_id, quantity_change): raise InventoryUpdateFailure("Not enough stock.") InventoryDatabase.update_stock(product_id, quantity_change) class Database: @staticmethod def commit(): # Commit the transaction pass @staticmethod def rollback(): # Rollback the transaction pass # Exception classes for clarity class InventoryUpdateFailure(Exception): pass class OrderProcessingFailure(Exception): pass # Example usage order_service = OrderService() try: order_service.process_order(order_id="1234", product_id="5678", quantity=1) print("Order processed successfully.") except OrderProcessingFailure as e: print(f"Error: {e}") Communication and Coordination between services The microservices architecture approach involves many services communicating with each other to provide the desired functionality. Services communicate with each other through APIs. Service coordination is essential to ensuring that the system works correctly. Testing communication and coordination between services can be challenging, especially when the number of services increases. Diverse Technology Stacks The challenge of a diverse technology stack in microservices testing stems from the inherent nature of microservices architecture, where each service is developed, deployed, and operated independently. This autonomy often leads to the selection of different technologies best suited for each service's specific functionality. While this flexibility is a strength of microservices, it also introduces several complexities in testing. 👉 Expertise in Multiple Technologies 👉 Environment Configuration 👉 Integration and Interface Testing 👉 Automated Testing Complexity 👉 Error Diagnosis and Troubleshooting 👉 Consistent Quality Assurance A financial services company uses different technologies for its microservices; some are written in Java, others in Python, and some use different databases. This diversity requires testers to be proficient in multiple technologies and complicates the setup of testing environments. Finding the root cause of failure When multiple services talk to each other, a failure can show up in any service, but the cause of that problem can originate from a different service deep down. Doing RCA for the failure becomes extremely tedious, time-consuming and high effort for teams of these distributed systems. Uber has over 2200 microservices in its web of interconnected services; if one service fails, all upstream services suffer the consequences. The more services there are, the more difficult it is to find the one that originated the problem. Unexpected Functional changes Uber decided to move to a distributed code base to break down application logic into several small repositories that can be built and deployed with speed. Though this gave teams the flexibility to make frequent changes, it also increased the speed at which new failures were introduced. A study by Dimensional Research found that the average cost of an hour of downtime for an enterprise is $300,000, highlighting the importance of minimizing unexpected functionality changes in microservices. So these rapid and continuous code changes, makes multi-repo systems more vulnerable to unintended breaking failures like latency, data manipulation etc. Difficulty in localizing the issue Each service is autonomous, but when it breaks, the failure it triggers can propagate far and wide, with damaging effects. This means the failure can show up elsewhere, but the trigger could be several services upstream. Hence, identifying and localizing the issue is very tedious, sometimes impossible without the right tools. How to overcome such challenges? Challenges like complexity and inter-service dependency are inherent to microservices. To tackle such intricacies, the conventional testing approach won’t work for testing these multi-repo systems. Since microservices themselves offer smarter architecture, testing them also needs a tailored approach. The usual method that follows unit testing , integration testing , and end-to-end testing won’t be the right one. The unit tests depend largely on mocks, making them less reliable, whereas E2E testing unnecessarily requires the whole system up and running as they test the complete user flow, leaving them tedious and expensive. You can find here how a tailored approach to test these independent services will help you take all these challenges away. A slight deviation from the traditional testing pyramid to a more suitable test pyramid for microservices is needed. The Solution Approach Microservices have a consumer-provider relationship between them. In a consumer-provider model, one microservice (the consumer) relies on another microservice (the provider) to perform a specific task or provide a specific piece of data. The consumer and provider communicate with each other over a network, typically using a well-defined API to exchange information. This means the consumer service could break irreversibly if the downstream service (provider) changes its response that the consumer is dependent on. So an approach that focuses on testing these contract schema between APIs to ensure the smooth functioning of services is needed. The easiest way to achieve this is to test every service independently for contracts [+data], by checking the API response of the service. In recent years, the trend of adopting microservices architecture has been increasing among organizations. This approach allows developers to build and deploy applications more quickly, enhance scalability, and promote flexibility. The HyperTest Way to Approach Microservices Testing HyperTest is a unique solution to run these contract[+data] tests or integration tests that can test end-to-end scenarios. It works on Real-time traffic replication (RTR), which monitors real user activity from production using a SDK set-up in your repo and automatically converts real-world scenarios into testable cases. These can be run locally or via CI to catch first-cut regressions and errors before a merge request moves to production. It implements these modes to test services: 👉Record Mode 👉Replay/ Test Mode Learn more about this approach here . HyperTest is an API test automation platform that helps teams generate and run integration tests for their microservices without ever writing a single line of code. It can use your application traffic to build integration tests in hours or days that can take teams months, if not years, to build. Not just that this builds very high coverage without effort, it by design makes it impossible for teams to introduce a breaking change or failure in your apps that is not first reported by HyperTest. HyperTest localizes the root cause of the breaking change to the right service very quickly, saving debugging time. 5 Best Practices For Microservices Testing Microservices testing is a critical aspect of ensuring the reliability and performance of applications built using this architectural style. Here are five best practices for microservices testing, each accompanied by an example for clarity: 1. Implement Contract Testing Contract testing ensures that microservices maintain consistent communication. It involves validating the interactions between different services against a contract, which defines how these services should communicate. Imagine a shipping service and an order service in an e-commerce platform. The order service expects shipping details in a specific format from the shipping service. Contract testing can be used to ensure that any changes in the shipping service do not break this expected format. 2. Utilize Service Virtualization Service virtualization involves creating lightweight, simulated versions of external services. This approach is useful for testing the interactions with external dependencies without the overhead of integrating with the actual services. In a banking application, virtualized services can simulate external credit score checking services. This allows testing the loan approval microservice without the need for the actual credit score service to be available. 3. Adopt Consumer-Driven Contract (CDC) Testing CDC testing is a pattern where the consumers (clients) of a microservice specify the expectations they have from the service. This helps in understanding and testing how consumers interact with the service. A mobile app (consumer) that displays user profiles from a user management microservice can specify its expected data format. The user management service tests against these expectations, ensuring compatibility with the mobile app. 4. Implement End-to-End Scenario Testing End-to-end scenario testing involves testing the entire application. It's crucial for ensuring that the entire system functions correctly as a whole. A tool like HyperTest works perfect for implementing this approach where all the scenarios will be covered without the need to keep the db, other services up and running. 5. Continuous Integration and Testing Continuously integrating and testing microservices as they are developed helps catch issues early. This involves automating tests and running them as part of the continuous integration pipeline whenever changes are made. A content management system with multiple microservices for article creation, editing, and publishing could use a CI/CD pipeline . Automated tests run each time a change is committed, ensuring that the changes don't break existing functionality. By following these best practices, teams can significantly enhance the quality and reliability of microservices-based applications. Each practice focuses on a different aspect of testing and collectively they provide a comprehensive approach to effectively handle the complexities of microservices testing. Conclusion Contract [+data] tests are-the optimal solution to test distributed systems. These service level contract tests are simple to build and easy to maintain, keeping the microservices in a ' releasable ' state. As software systems become more complex and distributed, testing each component individually and as part of a larger system can be a daunting task. We hope this piece has helped you with your search of finding the optimal solution to test your microservices. Download the ultimate testing guide for your microservices. Schedule a demo here to see how HyperTest fits in your software and never allows bugs to slip away. Related to Integration Testing Frequently Asked Questions 1. What Are Microservices? Microservices are a software development approach where an application is divided into small, independent components that perform specific tasks and communicate with each other through APIs. This architecture improves agility, allowing for faster development and scaling. It simplifies testing and maintenance by isolating components. If one component fails, it doesn't impact the entire system. Microservices also align with cloud technologies, reducing costs and resource consumption. 2. What tool is used to test microservices? HyperTest is a no-code test automation tool used for testing APIs. It works with an unique approach that can help developers automatically generate integration tests that test code with all its external components for every commit. It works on Real-time traffic replication (RTR), which monitors real user activity from production using a SDK set-up in your repo and automatically converts real-world scenarios into testable cases. These can be run locally or via CI to catch first-cut regressions and errors before a merge request moves to production. 3. How do we test microservices? Microservices testing requires an automated testing approach since the number of interaction surfaces keeps on increasing as the number of services grow. HyperTest has developed a unique approach that can help developers automatically generate integration tests that test code with all its external components for every commit. It works on Real-time traffic replication (RTR), which monitors real user activity from production using a SDK set-up in your repo and automatically converts real-world scenarios into testable cases. For your next read Dive deeper with these related posts! 10 Min. Read What is Microservices Testing? Learn More 05 Min. Read Testing Microservices: Faster Releases, Fewer Bugs Learn More 07 Min. Read Scaling Microservices: A Comprehensive Guide Learn More