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

Discover how automated integration testing accelerates development speed with these 5 powerful benefits. 28 March 2024 05 Min. Read Boost Dev Velocity with Automated Integration Testing WhatsApp LinkedIn X (Twitter) Copy link Download the Checklist Integration testing is crucial when it comes to microservices. The division of one entity into smaller components or modules and checking them if they all work together in sync as intended is the purpose of integration tests. Situated at the middle layer of the testing pyramid , integration testing focuses on validating the flow of data and functionality between various services. It primarily examines the input provided to a service and the corresponding output it generates, verifying that each component functions correctly when integrated with others. The integration of automation into the realm of integration testing significantly enhances its effectiveness. Automated integration tests offer numerous advantages, including improved code coverage and reduced effort in creating and maintaining test cases. This integration testing automation promises enhanced return on investment (ROI) by ensuring thorough testing with minimal manual intervention. This article is your go-to guide in case you are unaware of this combination and the enormous benefits that it brings along. Let’s dive right in: 1️⃣ Increased Test Coverage and Reliability Automation allows for a broader range of tests to be executed more frequently, covering more code and use cases without additional time or effort from developers. This comprehensive coverage ensures more reliable software, as it reduces the likelihood of untested code paths leading to bugs in production. 💡 With a more robust test suite, developers can make changes knowing they are less likely to cause disruptions. ✅ Achieve Up To 90% Test Coverage With HyperTest HyperTest can help you achieve high >90% of code coverage autonomously and at scale. It’s record-and-replay capabilities can reduce your testing efforts of 365 days to less than a few hours. HyperTest seamlessly integrates with microservices through an SDK, automatically capturing both inbound requests to a service and its outbound calls to external services or databases. This process generates comprehensive test cases that include all aspects of a service's interactions. 2️⃣ Reduced Time In Writing and Maintaining Test Cases Furthermore, the efficiency brought by automation greatly reduces the time and effort required in writing and maintaining test cases. 💡 Modern testing tools and frameworks  offer features that streamline test creation, such as reusable scripts and record-and-playback capabilities , while also simplifying maintenance through modular test designs. This not only accelerates the development cycle but also allows for rapid adaptation to changes in application code or user requirements. ✅ No need to write a single line of code with HyperTest When 39% of companies are interested in using codeless test automation tools , then why to still pursue tools that doesn’t gives you the freedom of codeless automation really. HyperTest is a simple to setup tool, requiring only 4 lines of code to be added to your codebase, and viola, HyperTest’s SDK is already working! Set it look at application traffic like an APM. Build integration tests with downstream mocks that are created and updated automatically. 3️⃣ Improved Speed to Run Test Cases The speed at which automated tests can be run is another critical advantage. Automated integration tests execute much faster than manual counterparts and can be run in parallel across different environments, significantly cutting down the time needed for comprehensive testing. This swift execution enables more frequent testing cycles, facilitating a faster feedback loop and quicker iterations in the development process. ✅ Autonomous Test generation in HyperTest paces up the whole process By eliminating the need to interact with actual third-party services, which can be slow or rate-limited, HyperTest significantly speeds up the testing process. Tests can run as quickly as the local environment allows, without being throttled by any external factors, which is the case in E2E tests. 4️⃣ Improved Collaboration and Reduced Silos Enhanced collaboration and reduced silos are also notable benefits of adopting automated integration testing. It promotes a DevOps culture, fostering cross-functional teamwork among development, operations, and quality assurance. With automation tools providing real-time insights into testing progress and outcomes, all team members stay informed, enhancing communication and collaborative decision-making. ✅ HyperTest instantly notifies you whenever a services gets updated HyperTest autonomously identifies relationships between different services and catches integration issues before they hit production. Through a comprehensive dependency graph, teams can effortlessly collaborate on one-to-one or one-to-many consumer-provider relationships. And whenever there’s a disruption in any service, HyperTest lets the developer of a service know in advance when the contract between his and other services has changed, enabling quick awareness and immediate corrective action. 5️⃣ Facilitates Continuous Integration and Deployment (CI/CD) Lastly, automated integration testing is pivotal for facilitating continuous integration and deployment (CI/CD) practices. It seamlessly integrates testing into the CI/CD pipeline , ensuring that code changes are automatically built, tested, and prepared for deployment. This capability allows for new changes to be rapidly and safely deployed, enabling organizations to swiftly respond to market demands and user feedback with high-quality software releases. ✅ Easy Integration of HyperTest with over 20+ CI/CD Tools HyperTest offers effortless integration with a wide range of Continuous Integration and Continuous Deployment (CI/CD) tools, including popular options like Jenkins, GitLab CI/CD, Travis CI, CircleCI, and many more. This seamless integration simplifies the incorporation of automated testing into the existing development workflow, ensuring that testing is seamlessly integrated into the deployment pipeline. By incorporating automated integration testing into their workflows, development teams can achieve higher velocity, deliver more reliable software faster, and respond more swiftly to market demands or changes. HyperTest can accelerate and help you achieve your goal of higher coverage with minimal test case maintenance, click here for a walk-through of HyperTest or contact us to learn more about its working approach. Related to Integration Testing Frequently Asked Questions 1. What are the best practices for conducting integration testing? Best practices for integration testing include defining clear test cases, testing early and often, using realistic test environments, automating tests where possible, and analyzing test results thoroughly. 2. How does integration testing contribute to overall software quality? Integration testing improves software quality by verifying that different modules work together correctly, detecting interface issues, ensuring data flows smoothly, identifying integration bugs early, and enhancing overall system reliability. 3. What are some common tools used for integration testing? Common tools for integration testing include HyperTest, SoapUI, JUnit, TestNG, Selenium, Apache JMeter, and IBM Rational Integration Tester. For your next read Dive deeper with these related posts! 08 Min. Read Best Integration Testing Tools in Software Testing Learn More 07 Min. Read Integration Testing Best Practices in 2024 Learn More 13 Min. Read What Is Integration Testing? Types, Tools & Examples Learn More

Uncover the essence of software testing through 7 principles with practical examples. Enhance your testing skills and ensure robust software quality. 19 April 2024 07 Min. Read 7 Principles of Software Testing With Examples WhatsApp LinkedIn X (Twitter) Copy link Access the Guide Software testing is an essential component in the creation of software, serving as a critical checkpoint to ensure that the final product meets the intended specifications and is free from flaws. It involves a systematic approach where software components are executed under controlled conditions to identify any discrepancies between the actual functionality and the expected output. Various forms of testing are employed throughout the development cycle, each serving a unique purpose. For example , unit testing focuses on individual parts of the software to ascertain their correctness, whereas integration testing examines the interaction between these parts. System testing scrutinizes the entire application for compliance with requirements, and acceptance testing verifies its suitability for delivery to the end users. The importance of software testing cannot be overstated. It is not merely about finding bugs; it's about ensuring software reliability, enhancing performance, and guaranteeing user satisfaction. In an era where software complexities are ever-increasing, adopting advanced testing methodologies like automated testing , continuous integration, and test-driven development is becoming the norm. These methodologies facilitate frequent and efficient testing, reducing the chances of late discovery of defects, which can be costly to fix. Why Software Testing is Important? Software testing holds a pivotal role in software development, primarily because it directly impacts the quality, reliability, and user satisfaction of the final product. Ensures Quality and Reliability : The primary goal of software testing is to ensure that the application is error-free and functions as intended. This rigorous process helps in identifying and rectifying bugs before the software reaches the end user. For instance, in a banking application, a minor error in transaction processing could have significant financial implications. Regular testing, such as regression testing every time a new feature is added, ensures that existing functionalities remain unaffected and reliable. def process_transaction(account_balance, transaction_amount): return account_balance - transaction_amount # Test to ensure transaction processing is correct assert process_transaction(1000, 200) == 800 User Satisfaction and Experience : In an increasingly user-centric world, delivering software that meets user expectations is key. Software testing includes usability testing to ensure the software is intuitive and user-friendly. For example, testing a mobile application's user interface for responsiveness and ease of navigation can significantly enhance user experience, thereby increasing customer retention and satisfaction. Cost-Effective in the Long Run : Identifying bugs in the early stages of development is less costly compared to fixing issues after deployment. The later a problem is discovered, the more expensive it is to fix. This is where test-driven development (TDD) and continuous integration come into play. By writing tests before the actual code, developers can catch potential issues early, reducing the cost and time spent on post-release fixes. Risk Mitigation : Software testing helps in mitigating risks associated with software failure. This is especially critical in applications where safety and security are paramount, such as in healthcare or aviation systems. By ensuring that the software operates under all conditions, testing reduces the risk of failures that could lead to catastrophic outcomes. Facilitates Continuous Improvement : Testing provides feedback to developers about the various aspects of software performance and usability. This feedback is crucial for continuous improvement in the software development process. Market Readiness : Ensuring that the software is free from critical bugs and is ready for the market is a key aspect of software testing. In competitive markets, releasing a bug-ridden product can severely damage a company's reputation and its bottom line. Types of Software Testing Software testing is an expansive field with various methodologies and approaches, each tailored to specific needs and stages of the software development lifecycle. Understanding the depth and breadth of these testing types is vital for making informed decisions and ensuring comprehensive quality assurance. Let’s have a closer look on these types of testing here : Static Testing : Unlike traditional dynamic testing where code is executed, static testing involves reviewing the code, requirement documents, and design documents. It's more preventive than detective in nature. Techniques include manual reviews, walkthroughs, and automated static analysis. This early-stage testing can identify issues before the code is run, saving time and resources. public class Calculator { public int add(int a, int b) { return a + b; } // A potential bug identified by static analysis: Unused parameter 'b' public int subtract(int a, int b) { return a; } } Dynamic Testing : This is the conventional method of testing where the software is executed to check for defects. It includes both functional testing (like system and integration testing ) and non-functional testing (like performance and usability testing). import static org.junit.Assert.*; import org.junit.Test; public class CalculatorTest { @Test public void testAdd() { Calculator calculator = new Calculator(); assertEquals(5, calculator.add(2, 3)); } } Exploratory Testing : This less structured approach relies on the tester's experience and intuition. Testers explore the software without predefined test cases, often uncovering issues that structured tests might miss. It’s particularly effective in complex, uncertain environments or when dealing with new features. Risk-Based Testing (RBT) : In RBT, the focus is on testing features and functions that are more likely to fail and would have the most significant impact if they did. This approach requires a thorough understanding of the application and its environment, making it a strategic choice for senior engineering personnel. Compatibility Testing : This type of testing checks the software's compatibility with different browsers, databases, operating systems, hardware, and mobile devices. It's crucial in today's diverse technological landscape, ensuring that software performs consistently across various platforms. Security Testing : With cybersecurity threats on the rise, security testing has become non-negotiable. This involves testing the software for vulnerabilities, threats, and risks which could lead to loss of information, revenue, and reputation. Performance Testing : This encompasses a set of tests to determine how a system performs in terms of responsiveness and stability under a particular workload. It includes load testing, stress testing, and spike testing. A/B Testing : Widely used in web development and user interface design, A/B testing involves comparing two versions of a web page or app to see which one performs better. It’s a practical approach to decision-making based on actual user data. Regression Testing : Essential in continuous development environments, regression testing ensures that new code changes don't adversely affect the existing functionality of the software. It's often automated to handle the repetitive nature of the tests. What are the Seven Fundamental Principles of Testing? Software testing is driven by a lot of principles as the world of software development takes an agile turn. But there are these seven fundamental principles of testing that provides a framework that guides effective software testing. These principles are rooted in decades of theory and practice in the field of software quality assurance. 1.Testing Shows the Presence of Defects : Testing can demonstrate that defects are present, but cannot prove that there are no defects. Testing reduces the probability of undiscovered defects remaining in the software but, even if no defects are found, it is not a guarantee of correctness. According to the Capers Jones report, effective testing can detect up to 85% of defects in software, but it's rare to identify every single issue. Example : Consider a function in a program that adds two numbers. Testing it with multiple pairs of numbers can show it works under those conditions, but can't guarantee it will work for all possible pairs. def add(a, b): return a + b # Test cases for the add function assert add(2, 3) == 5 assert add(-1, 1) == 0 2. Exhaustive Testing is Impossible : Testing everything (all combinations of inputs and preconditions) is not feasible except for trivial cases. Instead, risk analysis and priorities should be used to focus testing efforts. For a simple function with two inputs, each accepting 10 different values, there would be 100 test scenarios. As complexity increases, exhaustive testing becomes impractical. 3. Early Testing : The earlier testing is started in the software development lifecycle, the more cost-effective it is to identify and fix defects. IBM found that the cost to fix a bug is 6 times higher in implementation and 15 times higher post-release than during design. 4. Defect Clustering : A small number of modules usually contain most of the defects discovered during pre-release testing or are responsible for most operational failures. For example, in an e-commerce application, modules like payment processing and order management might contain more defects than others due to their complexity and frequent changes. 5. Pesticide Paradox : Repeatedly running the same set of tests over time will no longer find new defects. To overcome this, test cases need to be regularly reviewed and revised, adding new and different test cases to find more defects. Example : If a test suite for a web application only covers Chrome browser scenarios, it may miss defects that only appear in other browsers like Firefox or Safari. 6. Testing is Context-Dependent : Testing is done differently in different contexts. For example, safety-critical software (like that used in medical devices) is tested differently from an e-commerce website. 7. Absence-of-Errors Fallacy : Finding and fixing defects does not help if the system built is unusable and does not meet the users' needs and expectations. The primary goal should be to make software that is valuable and usable to the end user. Example : A feature-rich application with a complex interface may be technically sound but can fail due to poor user experience. Understanding and applying these principles helps in creating a more effective and efficient testing strategy, leading to the development of high-quality software that meets user needs and expectations. Conclusion The seven fundamental principles of software testing are more than just guidelines; they are the cornerstone of any robust and effective software testing strategy. For engineering heads and senior professionals, these principles are not just theoretical concepts; they are practical tools that can guide decision-making, strategy development, and day-to-day management of the testing process. See how these principles helped an ecommerce giant in ensuring the quality and security of its platform, critical for handling millions of transactions. By integrating these principles into their workflows, teams can avoid common pitfalls, maximize resources, and, most importantly, deliver software products that stand the test of time in terms of quality, performance, and user satisfaction. In an industry that is continually evolving, adherence to these timeless principles is key to staying ahead and ensuring the delivery of exceptional software products. Frequently Asked Questions 1. What are the First Principles of Testing? First principles of testing involve understanding objectives, planning, creating thorough test cases, executing systematically, analyzing results critically, and continuous improvement. Emphasizing clarity, simplicity, and effectiveness ensures a robust testing foundation. 2. What are the pillars of software testing? The pillars of software testing include thorough test planning, effective test design, meticulous test execution, comprehensive defect tracking, and continuous test improvement. These pillars form the essential framework for ensuring the quality and reliability of software products. 3. What are the stages of software testing? Software testing comprises stages like requirement analysis, test planning, test design, test execution, defect tracking, and test closure. These sequential phases ensure systematic validation and verification, promoting the delivery of high-quality software. 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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

Get automated tests that help devs identify and fix bad code faster and reduce technical debt in half the time WEBINAR | On-Demand | "No More Writing Mocks: The Future of Unit & Integration Testing" >> Get more time for innovation. Spend less fixing past issues. Get automated tests that help devs identify and fix bad code faster and reduce technical debt in half the time Get a Demo Tour the Platform Developers at the most innovative companies trust HyperTest for confident releases Slow Test suites When the test suite is build using the false promising E2E tests, causing brittleness, it can often take hours or even days to complete, delaying feedback and slowing down development. Poor Test Coverage Not covering enough user scenario’s and testing just from the UI front can leave critical parts of the codebase unprotected, increasing the risk of bugs and system failures. Developer Burnout When the devs are stuck with things such legacy codebase, frequent test failures, and the pressure to deliver quickly, it naturally boils them down as frustrated engineers. Longer Release Cycles Lengthy release cycles caused by unclear project goals, extensive testing and debugging, hindering time-to-market and business agility. Without HyperTest Light-weight superfast tests Each test created by HyperTest can be completed in just a few minutes and are super fast since they run directly from the CLI. This accelerated feedback loop powers rapid iteration and development. Get >90% Code Coverage Missed deadlines lead to frustrated customers waiting on promised features, impacting brand reputation and customer loyalty. Improved Developer Productivity Competitors who deliver on time can gain market share while your team struggles to catch up. Faster Releases With HyperTest Hear from our Customers HyperTest has been a game-changer for us in API 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 How it Works For Developers For Engineering Leaders Why Should Developers Use it? Get Powerful Integration Tests Test code, APIs, data layer and message queues end to end at the same time Automate Testing with Self-healing Mocks Use mocks that mimic external interfaces to test user behavior not just code Shift left like it needs to be Run tests locally with pre-commit hooks or at CI to catch issues early and fast 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. 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 2023 Year 8,547 Test Runs 8 million+ Regressions 100+ Product Teams Prevent Logical bugs in your database calls, queues and external APIs or services Calculate your ROI

Discover the power of Shift Left Testing Approach: Boost efficiency, catch bugs early, and enhance software quality with our step-by-step guide on how to implement Shift Left Testing. 12 March 2024 10 Min. Read How To Implement Shift Left Testing Approach WhatsApp LinkedIn X (Twitter) Copy link Get 101 Guide In the rapidly evolving world of software development, the quest for efficiency and quality has led to the adoption of various methodologies aimed at enhancing the software development lifecycle (SDLC). Among these, Shift Left Testing has emerged as a pivotal approach, fundamentally altering how testing is integrated within the development process. This method advocates for initiating testing activities earlier in the SDLC, thereby shifting the testing phase to the "left" on the project timeline. The essence of Shift Left Testing lies in its preventative philosophy, aiming to detect and address defects in the early stages of software development rather than at the end. Shift Left Testing approach represents not just a methodology but a cultural transformation towards embracing quality as a foundational aspect of software development. It is a testament to the industry's ongoing evolution, reflecting a deeper understanding of the dynamics between speed, quality, and efficiency in creating software that meets and exceeds the demands of users and stakeholders alike. The Systems Sciences Institute at IBM revealed that addressing a bug discovered in the implementation phase is six times more expensive than rectifying one identified during the design phase. Additionally, IBM indicates that the expense of fixing bugs detected in the testing phase could be fifteen times higher than the cost of addressing those identified during the design phase. Importance of Shift Left Testing The Shift Left Testing approach is gaining traction within the software development community, not merely as a trend but as a significant evolution in enhancing the quality and reliability of software products. Here are some of the key advantages that make Shift Left Testing particularly appealing to everyone looking to release faster and save costly bug detection at later stages: ➡️Early Bug Detection and Resolution At the heart of Shift Left Testing is the principle of early detection and resolution of bugs. By testing early and often in the development cycle, bugs are identified before they can evolve into more complex and costly problems. This early intervention is not just a cost-saving measure; it significantly improves the stability and reliability of the software. For devs, this means less time spent backtracking to resolve issues in code that was considered complete, allowing for a more streamlined and efficient development process. ➡️Enhanced Collaboration and Communication Shift Left Testing fosters an environment of enhanced collaboration and communication between developers, testers, and operations teams. This collaborative approach ensures that quality is a shared responsibility and not just relegated to testers at the end of the development pipeline. Receiving immediate feedback on their code, enabling quick adjustments that align with both functional and quality requirements is a key factor for agile developers. ➡️Reduction in Development Costs and Time By identifying and addressing defects early, Shift Left Testing approach significantly reduces the cost and time associated with fixing bugs in later stages of development or after release. The cost of fixing a bug after deployment can be exponentially higher than fixing it during the development phase. It leads to more predictable development timelines, reduced pressure to fix issues under tight deadlines, and a decrease in the overall cost of development projects. ➡️Improved Product Quality and Customer Satisfaction Shift Left Testing inherently leads to a better end product. With continuous testing and quality assurance from the start, the software is built on a foundation of quality, resulting in a more stable, performant, and secure application. This not only enhances the reputation of the development team but also fosters a positive relationship with the end-users, who benefit from a superior product experience. ➡️Supports Agile and DevOps Practices The Shift Left Testing approach is perfectly aligned with Agile and DevOps practices, which emphasize rapid development cycles, continuous integration (CI), and continuous delivery (CD). For devs working in Agile environments, Shift Left Testing provides the framework for integrating testing into each sprint, ensuring that every iteration of the product is tested and validated. This seamless integration of testing into the CI/CD pipeline enables faster release cycles, with the confidence that each release maintains a high standard of quality. Implementing Shift Left Testing Approach Implementing Shift Left Testing in a development project involves a strategic shift in how testing is integrated into the software development lifecycle. This not only necessitates changes in processes and tools but also in the mindset of developers and testers. Step 1: Integrate Testing into the Early Stages of Development Example : Incorporate unit testing as part of the development process. Developers should write unit tests for their code before or alongside the development of the features. # Example of a simple Python unit test for a function add(a, b) import unittest def add(a, b): return a + b class TestAddFunction(unittest.TestCase): def test_add(self): self.assertEqual(add(2, 3), 5) self.assertEqual(add(-1, 1), 0) self.assertEqual(add(-1, -1), -2) if __name__ == '__main__': unittest.main() Step 2: Leverage Automation for Continuous Testing Automate your testing processes to run tests continuously as code is integrated into the main branch. This involves setting up CI pipelines that automatically trigger tests upon code commits. Example : Configure a CI pipeline using Jenkins, GitHub Actions, or GitLab CI to run your automated tests whenever new code is pushed to the repository. # Example of a basic GitHub Actions workflow to run Python unit tests name: Python Unit Tests on: [push] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.8' - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt - name: Run tests run: | python -m unittest discover -s tests Step 3: Foster a Culture of Quality and Collaboration Shift Left Testing requires a cultural shift where quality is everyone's responsibility. Encourage developers, testers, and operations teams to collaborate closely from the project's inception. Example : Implement pair programming sessions between developers and testers to discuss test strategies for new features. Use communication tools like Slack or Microsoft Teams to facilitate continuous dialogue around testing and quality. Step 4: Implement Test-Driven Development (TDD) TDD is a key practice in Shift Left Testing, where you write tests for a new feature before writing the code itself. // Example of TDD for a simple JavaScript function to check if a number is even // Step 1: Write the test function isEven(num) { // Test function will be implemented here } describe("isEven", function() { it("returns true if the number is even", function() { assert.equal(isEven(4), true); }); it("returns false if the number is odd", function() { assert.equal(isEven(5), false); }); }); // Step 2: Implement the function to pass the test function isEven(num) { return num % 2 === 0; } Step 5: Embrace Feedback and Iteration Use feedback from testing to continuously improve the software. Implement tools and practices that make it easy to report, track, and address issues discovered during testing. Example : Utilize issue tracking systems like Jira or GitHub Issues to manage feedback from tests, ensuring that all stakeholders can view the status of issues and contribute to their resolution. Best Practices and Techniques for Shift Left Testing Adopting Shift Left Testing in software development projects involves more than just an early start to testing; it encompasses a set of best practices and techniques designed to optimize the process. These practices ensure that testing is not only proactive but also integrated seamlessly into the development lifecycle, contributing to the overall quality and efficiency of the project. Here are key best practices and techniques that agile teams should consider: 1. Embed Quality Assurance in Every Phase Quality assurance (QA) should be a continuous concern, starting from the initial stages of development. This means integrating QA processes and considerations into the planning, design, coding, and deployment phases. It's crucial to establish quality criteria and testing goals early on, ensuring that each feature developed meets the predefined standards before moving forward. 2. Leverage Automation Wisely While manual testing remains valuable for certain types of tests (e.g., exploratory testing), automation is a cornerstone of effective Shift Left Testing. Automate repetitive and time-consuming tests, such as regression tests, to save time and ensure consistency. However, be selective in what you automate to avoid maintaining a cumbersome suite of tests that might become obsolete quickly. Focus on automating tests that provide the most value and are likely to be reused. // Example: Automating a simple login test with Selenium WebDriver in JavaScript const {Builder, By, Key, until} = require('selenium-webdriver'); async function exampleTest() { let driver = await new Builder().forBrowser('firefox').build(); try { await driver.get(''); await driver.findElement(By.id('username')).sendKeys('testUser'); await driver.findElement(By.id('password')).sendKeys('testPassword', Key.RETURN); await driver.wait(until.titleIs('Dashboard'), 1000); } finally { await driver.quit(); } } exampleTest(); 3. Practice Test-Driven Development (TDD) TDD is a powerful technique in Shift Left Testing, where developers write tests before writing the code that implements the functionality. This approach ensures that development is guided by tests, leading to code that is inherently more testable and aligned with the specified requirements. 4. Prioritize CI/CD CI/CD practices are integral to Shift Left Testing, enabling continuous testing, integration, and deployment of code changes. Implement a CI/CD pipeline that automatically runs your test suite against every commit to the codebase, ensuring immediate feedback on the impact of changes. 5. Foster a Collaborative Culture Shift Left Testing requires a culture of collaboration between developers, testers, and other stakeholders. Encourage open communication and shared responsibility for quality, breaking down the silos that traditionally separate development and testing teams. Tools like pair programming and code reviews can facilitate this collaboration, allowing developers and testers to share insights and knowledge. 6. Optimize Test Environments Ensure that your testing environments closely mimic the production environment to uncover issues that could affect users. Use containerization tools like Docker to create consistent, easily replicable testing environments that can be spun up or down as needed. 7. Embrace Feedback Loops Implement short, iterative development cycles that incorporate feedback from testing early and often. Use the results from each testing phase to refine and improve both the product and the testing process itself. Challenges and Solutions Implementing Shift Left Testing offers numerous benefits, including early detection of defects, improved code quality, and a more efficient development process. However, organizations may face several challenges while adopting this approach. Recognizing these challenges and identifying effective solutions is crucial for a smooth transition to Shift Left Testing. Challenges Cultural Resistance : Shifting testing left requires a cultural shift within the organization, moving away from traditional development and testing silos. Developers, testers, and operations teams must collaborate closely, which can be a significant change for teams used to working independently. Skill Gaps : As testing moves earlier into the development cycle, developers may need to acquire new testing skills, and testers may need to gain a deeper understanding of the code. This skill gap can hinder the effective implementation of Shift Left Testing. Integration Complexity : Incorporating testing into the early stages of development and ensuring continuous integration and delivery (CI/CD) can be technically complex. Setting up automated testing frameworks and CI/CD pipelines requires careful planning and execution. Maintaining Test Quality and Coverage : As the amount of testing increases, maintaining high-quality and comprehensive test coverage becomes challenging. Ensuring that tests are effective and cover the necessary aspects of the code without becoming redundant or obsolete is crucial. HyperTest To Mitigate These Challenges HyperTest using its CLI can integrate natively with any CI tool used for automated releases, and tests every new change or update in the application automatically with a new PR or commit as the trigger When a PR is raised by the dev using GitHub, GitLab, Bitbucket or any other version control system, 2 things happen: Their CI server would receive a new event notification which would then let it build and deploy the app. The native CLI utility of HyperTest is in parallel notified of these events, making it automatically run all the tests. The best part is that the final report that HyperTest generates can be viewed by devs inside their VCS, without ever moving out. Implementing Shift Left Testing, complemented by tools like HyperTest, allows teams to overcome the challenges associated with this approach. It enables a smoother transition to a testing paradigm that places quality at the forefront of software development, ensuring that applications are not only built right but also built to last. For more insights into how HyperTest simplifies microservices testing, visit the website . Use Case in FinTech Industry Background XYZ Corporation, a leading software development firm specializing in financial services applications, faced increasing pressure to accelerate its product development lifecycle while maintaining high standards of quality and security. With the growing complexity of their applications and the need for faster release cycles, the traditional approach to testing was becoming a bottleneck, leading to delays in releases and increased costs associated with late-stage defect resolution. Challenge The primary challenge for XYZ Corporation was: Reducing the time-to-market for new features and Updates while ensuring that the software remained secure, reliable, and user-friendly. The late discovery of bugs in the development cycle was leading to costly fixes, delayed product launches , and a negative impact on customer satisfaction. Implementation of Shift Left Testing To address these challenges, XYZ Corporation decided to invest in a Shift Left Testing approach. The initiative involved several key steps: Integration of Automated Testing Tools : XYZ Corporation integrated automated testing tools into their CI/CD pipelines, enabling tests to be run automatically with every code commit. Adoption of TDD : Developers were encouraged to adopt TDD practices, writing tests before writing the actual code to ensure that all new features met the testing criteria from the outset. Enhanced Collaboration between Developers and Testers : The company fostered a culture of collaboration between the development and testing teams. Results The implementation of Shift Left Testing led to significant improvements for XYZ Corporation: Reduced Time-to-Market : The time required to release new features and updates was reduced by 30%, thanks to early bug detection and resolution. Improved Product Quality : The number of critical defects found after release decreased by 50%, resulting in higher customer satisfaction and reduced support costs. Increased Efficiency : The development team reported a 20% increase in efficiency, as early testing reduced the need for extensive rework. Conclusion Quick fixes, big savings! The adoption of Shift Left Testing represents a pivotal transformation in the software development lifecycle, offering a proactive approach to quality assurance that brings significant benefits to both the development process and the final product. By integrating testing early and throughout the development cycle, organizations can achieve faster release times, enhanced product quality, reduced costs, and improved team collaboration and morale . In conclusion, Shift Left Testing is not merely a trend but a fundamental shift towards embedding quality deeply and consistently into software development. It offers a pathway to building software that not only meets but exceeds the demands of today's fast-paced, quality-conscious market. For organizations aiming to stay competitive and innovative, embracing Shift Left Testing is not just an option but a necessity. Related to Integration Testing Frequently Asked Questions 1. How do you implement shift-left testing? Shift-left testing is implemented by moving testing activities earlier in the software development process, identifying and addressing issues sooner. 2. What is the best software testing tool? In performance testing, the shift-left approach involves conducting performance testing earlier in the development lifecycle to detect and address performance issues sooner. 3. What are the types of shift left testing? Types of shift-left testing include unit testing, static code analysis, and early integration testing. For your next read Dive deeper with these related posts! 07 Min. Read Shift Left Testing: Types, Benefits and Challenges Learn More 09 Min. Read What is System Testing? Types & Definition with Examples Learn More Add a Title What is Integration Testing? A complete guide Learn More

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Discover the critical role of unit testing in software development. Learn how it prevents bugs, improves code quality, and boosts developer confidence. 17 July 2024 07 Min. Read Importance and Purpose of Unit Testing in Software Engineering WhatsApp LinkedIn X (Twitter) Copy link Get a Demo Unit testing, a cornerstone of modern software development, is often overlooked or underestimated. This blog delves into the critical importance and purpose of unit testing, providing insights into its benefits and best practices. What is Unit Testing? Unit testing is a fundamental practice in software engineering where individual components or units of a software application are tested in isolation. Each unit, typically the smallest testable part of the software such as a function or method, is scrutinised to ensure it performs as expected. The purpose of unit testing is to validate that each unit of the software code operates correctly, thereby catching bugs early in the development process. Developers can pinpoint and resolve issues more efficiently by isolating and testing units independently . This practice not only improves code quality and reliability but also simplifies debugging and maintenance. Unit testing involves isolating individual components of a software system and verifying their correct behavior. These components, often referred to as "units," could be functions, methods, or classes. The primary goal is to ensure that each unit performs its intended task accurately and reliably. Prerequisites of Unit Testing Before embarking on unit testing, certain prerequisites must be met to ensure its effectiveness. Meeting these prerequisites is fundamental to achieving the primary purpose of unit testing, which is to identify and fix defects early in the development cycle. Firstly, a well-defined and modular codebase is essential. Code should be broken down into small, manageable units or functions that perform single, well-defined tasks. This modularity is necessary for isolating units during testing. Secondly, a comprehensive understanding of the application's requirements and functionality is necessary. This ensures that the tests align with the intended behaviour of each unit. Clear documentation and specifications serve as a guide for creating meaningful and relevant test cases. Another prerequisite is the establishment of a testing framework or tool. Popular frameworks like JUnit for Java, NUnit for .NET and PyTest for Python provide the necessary infrastructure for writing and executing unit tests efficiently. Additionally, developers must have a good grasp of writing testable code. This involves adhering to best practices such as dependency injection and avoiding tightly coupled code, which makes units easier to test in isolation. 💡 Avoid the tedious process of writing and maintaining the test code and engage in an advanced practice of code-based unit testing, learn the approach here. Lastly, maintaining a clean and controlled test environment is critical. Tests should run in an environment that closely mirrors the production setup to ensure reliability. Key Principles of Effective Unit Testing Isolation: Each unit test should focus on a single unit, minimizing dependencies on external factors. Independence: Unit tests should be independent of each other to avoid cascading failures. Repeatability: Tests should produce the same results consistently across different environments. Fast Execution: Unit tests should run quickly to facilitate frequent execution. Readability: Tests should be well-structured and easy to understand, promoting maintainability. Types of Unit Testing Unit testing can be classified into several types, each serving distinct purposes in ensuring the functionality of individual software units. The primary types include: Manual Unit Testing : This involves developers manually writing and executing test cases. Though time-consuming and prone to human error, manual testing is useful for understanding the software's behaviour and for scenarios where automated testing is not feasible. Automated Unit Testing : Utilising testing frameworks and tools, developers automate the execution of test cases. This type is highly efficient, allowing for frequent and repetitive testing with minimal effort. Automated unit testing enhances accuracy and consistency, significantly reducing the chances of human error. White-box Testing : Also known as clear or glass box testing, this type focuses on the internal structures and workings of the software. Testers need to understand the internal code and logic to create test cases that ensure each path and branch is tested thoroughly. Black-box Testing : This type ignores the internal code and focuses solely on the inputs and expected outputs. Testers do not need to know the internal implementation, making it useful for validating the software's functionality against its specifications. Grey-box Testing : Combining elements of both white-box and black-box testing, grey-box testing requires testers to have partial knowledge of the internal workings. This type strikes a balance, allowing testers to create more informed test cases while still validating external behaviour. Read more - Different Types of Unit Testing Importance of Unit Testing Unit testing holds high importance in software development due to its numerous benefits in ensuring code quality and reliability. The primary purpose of unit testing is to validate that individual components of the software function correctly in isolation. Developers can identify and rectify defects early in the development cycle by testing these smaller units independently, thus significantly reducing the cost and effort required for later stages of debugging and maintenance. The importance of unit testing extends beyond merely catching bugs. It develops a modular codebase, as developers are encouraged to write code that is easily testable. This leads to better-designed, more maintainable and scalable software. Additionally, unit testing provides a safety net for code changes, ensuring that new updates or refactoring efforts do not introduce new bugs. This continuous verification process is crucial for maintaining high software quality over time. Moreover, unit tests serve as documentation for the codebase, offering insights into the expected behaviour of various components. This is particularly valuable for new team members who need to understand and work with existing code. In essence, the purpose of unit testing is twofold — to ensure each part of the software performs as intended and to facilitate ongoing code improvement and stability. Conclusion Unit testing is indispensable for developing high-quality, reliable software. Because it ensures each component functions correctly, it helps catch defects early, supports code modularity and provides a safety net for changes. HyperTest is an advanced testing framework that automates the unit testing process, offering high-speed execution and auto-maintenance of mocks. It integrates seamlessly with various development environments, making it a versatile option for different programming languages and platforms. HyperTest's ability to rapidly identify and fix bugs aligns perfectly with the primary purpose of unit testing, which is to ensure error-free code. Its user-friendly interface and powerful features make it an excellent choice for developers looking to streamline their unit testing efforts. Because HyperTest is primarily an API and integration testing tool built for developers, it can significantly improve the efficiency and effectiveness of the unit testing process too, thereby leading to more dependable and maintainable software. For more on HyperTest, visit here . Related to Integration Testing Frequently Asked Questions 1. What are the prerequisites for unit testing? To perform unit testing, you need a solid understanding of the programming language, development environment, and the codebase. A grasp of testing concepts, test-driven development, and mocking frameworks is also beneficial. 2. What testing frameworks are commonly used? Popular unit testing frameworks include JUnit for Java, NUnit for .NET, pytest for Python, and Jest for JavaScript. These frameworks provide tools for writing, organizing, and running tests efficiently. 3. What is the main purpose of unit testing? The primary goal of unit testing is to verify the correctness of individual code units (functions or methods) in isolation. This helps identify bugs early, improve code quality, and facilitate code changes with confidence. For your next read Dive deeper with these related posts! 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 05 Min. Read Different Types of Unit Testing: A Comprehensive Overview Learn More

Explore the distinctions between frontend vs backend testing, uncovering key differences in methodologies, tools, and objectives. 22 January 2024 07 Min. Read Frontend Testing vs Backend Testing: Key Differences WhatsApp LinkedIn X (Twitter) Copy link Download the 101 guide In the intricate world of software development, testing is a critical phase that ensures the quality and functionality of applications. Two primary testing areas, often discussed in tandem but with distinct characteristics, are frontend and backend testing. This article delves into the nuances of these testing methodologies, highlighting their key differences and importance in the software development lifecycle. Understanding Frontend Testing Frontend testing primarily focuses on the user interface and experience aspects of a software application. It involves verifying the visual elements that users interact with, such as buttons, forms, and menus, ensuring that they work as intended across different browsers and devices. This type of testing is crucial for assessing the application's usability, accessibility, and overall look and feel. Types of Frontend Testing In the realm of frontend testing, various testing methods contribute across different stages of the testing process. For instance, unit testing occurs during the early stages of the software development life cycle, followed by component testing and integration testing . In essence, the frontend testing of an application encompasses the execution of diverse testing approaches on the targeted application. The following are some commonly employed types of tests: 1. User Interface (UI) Testing: Tests the graphical interface to ensure it meets design specifications. Tools : Selenium, Puppeteer. Example : Ensuring buttons, text fields, and images appear correctly on different devices. 2. Accessibility Testing: Ensures that the application is usable by people with various disabilities. Tools : Axe, WAVE. Example : Verifying screen reader compatibility and keyboard navigation. 3. Cross-Browser Testing: Checks how the application behaves across different web browsers. Tools : BrowserStack, Sauce Labs. Example : Ensuring consistent behavior and appearance in Chrome, Firefox, Safari, etc. 4. Performance Testing: Ensures the application responds quickly and can handle expected load. Tools : Lighthouse, WebPageTest. Example : Checking load times and responsiveness under heavy traffic. Best Practices in Frontend Testing Automate Where Possible : Automated tests save time and are less prone to human error. Prioritize Tests : Focus on critical functionalities like user authentication, payment processing, etc. Responsive Design Testing : Ensure the UI is responsive and consistent across various screen sizes. Continuous Integration/Continuous Deployment (CI/CD) : Integrate testing into the CI/CD pipeline for continuous feedback. Test Early and Often : Incorporate testing early in the development cycle to catch issues sooner. Use Realistic Data : Test with data that mimics production to ensure accuracy. Cross-Browser and Cross-Device Testing : Validate compatibility across different environments. Accessibility Compliance : Regularly check for compliance with accessibility standards like WCAG. Performance Optimization : Regularly test and optimize for better performance. Involve End Users : Conduct user testing sessions for real-world feedback. Example Code Block for Unit Testing with Jest Let's consider a simple React component and a corresponding Jest test: React Component (Button.js): import React from 'react'; function Button({ label }) { return {label}; } export default Button; Jest Test (Button.test.js): import React from 'react'; import { render } from '@testing-library/react'; import Button from './Button'; test('renders the correct label', () => { const { getByText } = render(); const buttonElement = getByText(/Click Me/i); expect(buttonElement).toBeInTheDocument(); }); In this example, we're using Jest along with React Testing Library to test if the Button component correctly renders the label passed to it. Frontend testing is a vast field, and the approach and tools may vary based on the specific requirements of the project. It's crucial to maintain a balance between different types of tests while ensuring the application is thoroughly tested for the best user experience. Diving into Backend Testing In contrast, backend testing targets the server-side of the application. This includes databases, servers, and application logic. Backend testing is essential for validating data processing, security, and performance. It involves tasks like database testing, API testing , and checking the integration of various system components. Types of Backend Testing 1. Unit Testing : Testing individual units or components of the backend code in isolation. Tools : JUnit (Java), NUnit (.NET), PyTest (Python). Example : Testing a function that calculates a user's account balance. 2. Integration Testing : Testing the interaction between different modules or services in the backend. Tools : Postman, SoapUI. Example : Testing how different modules like user authentication and data retrieval work together. 3. Functional Testing : Testing the business requirements of the application. Tools : HP ALM, TestRail. Example : Verifying if a data processing module correctly generates reports. 4. Database Testing: Ensuring the integrity and consistency of database operations, data storage, and retrieval. Tools : SQL Developer, DbUnit. Example : Checking if a query correctly retrieves data from a database table. 5. API Testing : Testing the application programming interfaces (APIs) for functionality, reliability, performance, and security. Tools : Postman, HyperTest, Swagger. Example : Verifying if an API returns the correct data in response to a request. 6. Performance Testing: Evaluating the speed, scalability, and stability of the backend under various conditions. Tools : Apache JMeter, LoadRunner. Example : Assessing the response time of a server under heavy load. 7. Security Testing: Identifying vulnerabilities in the backend and ensuring data protection. Tools : OWASP ZAP, Burp Suite. Example : Testing for SQL injection vulnerabilities. 8. Load Testing: Testing the application's ability to handle expected user traffic. Tools : LoadRunner, Apache JMeter. Example : Simulating multiple users accessing the server simultaneously to test load capacity. Best Practices in Backend Testing Comprehensive Test Coverage : Ensure all aspects of the backend, including databases, APIs, and business logic, are thoroughly tested. Automate Regression Tests : Automate repetitive tests to save time and reduce errors. Realistic Testing Environment : Test in an environment that closely resembles the production setting. Data-Driven Testing : Use varied and extensive datasets to test how the backend handles different data inputs. Prioritize Security : Regularly test for and fix security vulnerabilities. Monitor Performance Regularly : Continuously monitor server performance and optimize when necessary. Version Control for Test Cases : Maintain a version control system for test documentation and scripts. CI/CD Integration : Integrate backend testing into the Continuous Integration/Continuous Deployment pipeline. Test Early and Often : Implement testing early in the development cycle and conduct tests frequently. Collaboration Between Teams : Encourage collaboration between backend developers, testers, and operations teams. HyperTest , our no-code API automation testing tool provides a quick remediation by notifying on disruption. It lets developer of a service know in advance when the contract between his and other services has changed, offering immediate action and better collaboration. Example Code Block for API Testing with Postman Assuming you have an API endpoint /api/users for retrieving user data, you can create a test in Postman: Send a GET request to /api/users. In the "Tests" tab of Postman, write a test script to validate the response: pm.test("Status code is 200", function () { pm.response.to.have.status(200); }); pm.test("Response time is less than 500ms", function () { pm.expect(pm.response.responseTime).to.be.below(500); }); pm.test("Response should be in JSON format", function () { pm.response.to.have.header("Content-Type", "application/json"); }); pm.test("Response contains user data", function () { var jsonData = pm.response.json(); pm.expect(jsonData.users).to.not.be.empty; }); In this example, Postman is used to validate the status code, response time, content type, and data structure of the API response. With all the API collections, API testing becomes all the way tedious and time-consuming with Postman eventually. HyperTest is a way out here, you won’t need to manually write test scripts for all the APIs you have. Here’s a quick overview on Postman Vs HyperTest. Frontend vs. Backend Testing: Key Differences Layer of Testing : Frontend Testing: Focuses on the presentation layer. Backend Testing: Concentrates on the application and database layers. Nature of Testing : Frontend Testing: Involves graphical user interface (GUI) testing, layout, and responsiveness. Backend Testing: Encompasses database integrity, business logic, and server testing. Technical Expertise : Frontend Testing: Requires knowledge of HTML, CSS, JavaScript, and design principles. Backend Testing: Demands proficiency in database management, server technology, and backend programming languages. Tools and Techniques : Frontend Testing: Utilizes tools like Selenium, Jest, and Mocha for automation and unit testing. Backend Testing: Employs tools like Postman, SQL databases, and server-side testing frameworks. Challenges and Focus Areas : Frontend Testing: Challenges include cross-browser compatibility and maintaining a consistent user experience. Backend Testing: Focuses on data integrity, performance optimization, and security vulnerabilities. Aspect Front-End Testing Back-End Testing Primary Focus User Interface, User Experience Database, Server, API Testing Objectives - Ensure visual elements function correctly - Validate responsiveness and interactivity - Check cross-browser compatibility - Validate database integrity - Test server-side logic - Ensure API functionality and performance Tools Used - Selenium - Jest - Cypress - Mocha - Postman - JUnit - HyperTest -TestNG Challenges - Browser compatibility - Responsive design issues - Database schema changes - Handling large datasets Types of Tests - UI Tests - Cross-Browser Tests - Accessibility Tests - Unit Tests - Integration Tests - API Tests Key Metrics - Load time - User flow accuracy - Query execution time - API response time Skill Set Required - HTML/CSS/JavaScript knowledge - Design principles - SQL/NoSQL knowledge - Understanding of server-side languages Integration with Other Systems Often requires mock data or stubs for back-end services Typically interacts directly with the database and may require front-end stubs for complete testing End-User Impact Direct impact on user experience and satisfaction Indirect impact, primarily affecting performance and data integrity Common Issues Detected - Layout problems - Interactive element failures - Data corruption - Inefficient database queries Why Both Frontend and Backend Testing are Vital? Both frontend and backend testing offer unique values: Frontend testing ensures that the user-facing part of the application is intuitive, responsive, and reliable. Backend testing ensures that the application is robust, secure, and performs well under various conditions. Conclusion Frontend Testing vs Backend Testing is a never ending debate though. But as we know by now how crucial they both are in their own perspective to keep an app running and thoroughly tested. So, as we understand frontend and backend testing serve different purposes and require distinct skills, they are equally important in delivering high-quality software products. A balanced approach, incorporating both testing methodologies, ensures a robust, user-friendly, and secure application, ready to meet the demands of its end-users. Related to Integration Testing Frequently Asked Questions 1. Which is better frontend or backend testing? Neither is inherently better; both are essential. Frontend testing ensures user interface correctness and usability, while backend testing validates server-side functionality, data processing, and integration. 2. How many types of QA are there? Selenium is primarily a frontend testing tool. It automates web browsers to test user interfaces. 3. Which tool is best for backend testing? HyperTest is a powerful choice for backend testing, known for its efficiency in API testing. It offers fast and thorough validation of backend services, making it a preferred tool in modern development environments. 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 What is Functional Testing? Types and Examples Learn More Add a Title What is Integration Testing? A complete guide Learn More

Learn how to easily compare JSON files and find differences using tools and techniques for efficient analysis and debugging. 19 March 2025 07 Min. Read The Developer's Guide to JSON Comparison: Tools and Techniques WhatsApp LinkedIn X (Twitter) Copy link Try JSON Comparison Tool Now Ever deployed a breaking change that was just a missing comma? It's Monday morning. Your team just deployed a critical update to production. Suddenly, Slack notifications start flooding in—the application is down. After frantic debugging, you discover the culprit: a single misplaced key in a JSON configuration file. What should have been "apiVersion": "v2" was accidentally set as " apiVerison": "v2 " . A typo that cost your company thousands in downtime and your team countless stress-filled hours. This scenario is all too familiar to developers working with JSON data structures. The reality is that comparing JSON files effectively isn't just a nice-to-have skill—it's essential for maintaining system integrity and preventing costly errors. Stack Overflow's 2024 Developer Survey shows 83% of developers prefer JSON over XML or other data formats for API integration. What is a JSON File? JSON (JavaScript Object Notation) is a lightweight data interchange format that has become the lingua franca of web applications and APIs. It's human-readable, easily parsable by machines, and versatile enough to represent complex data structures. A simple JSON object looks like this: { "name": "John Doe", "age": 30, "city": "New York", "active": true, "skills": ["JavaScript", "React", "Node.js"] } JSON files can contain: Objects (enclosed in curly braces) Arrays (enclosed in square brackets) Strings (in double quotes) Numbers (integer or floating-point) Boolean values (true or false) Null values The nested and hierarchical nature of JSON makes it powerful but also introduces complexity when comparing files for differences. Why comparing JSON files is critical? JSON comparison is essential in numerous development scenarios: Scenario Why JSON Comparison Matters API Development Ensuring consistency between expected and actual responses Configuration Management Detecting unintended changes across environments Version Control Tracking modifications to data structures Database Operations Validating data before and after migrations Debugging Isolating the exact changes that caused an issue Quality Assurance Verifying that changes meet requirements Without effective comparison tools, these tasks become error-prone and time-consuming, especially as JSON structures grow in complexity. Common JSON Comparison Challenges Before diving into solutions, let's understand what makes JSON comparison challenging: Order Sensitivity : JSON objects don't guarantee key order, so {"a":1,"b":2} and {"b":2,"a":1} are semantically identical but may be flagged as different by naive comparison tools. Whitespace and Formatting : Differences in indentation or line breaks shouldn't affect comparison results. Type Coercion : String "123" is not the same as number 123, and comparison tools need to respect this distinction. Nested Structures : Deeply nested objects make visual comparison nearly impossible. Array Order : Sometimes array order matters ([1,2,3] vs. [3,2,1]), but other times it doesn't (lists of objects where only the content matters). Methods for Comparing JSON Files 1. Visual Inspection The most basic approach is manually comparing JSON files side-by-side in your editor. This works for small files but quickly becomes impractical as complexity increases. Pros: No tools required Good for quick checks on small files Cons: Error-prone Impractical for large files Difficult to spot subtle differences With microservices now powering 85% of enterprise applications, JSON has become the standard interchange format, with an average enterprise managing over 100,000 JSON payloads daily. 2. Command Line Tools Command-line utilities offer powerful options for JSON comparison. ➡️ Using diff The standard diff command can compare any text files: diff file1.json file2.json For more readable output, you can use: diff -u file1.json file2.json The diff command in JSON format is particularly valuable for detecting schema drift between model definitions and actual database implementations. The structured output can feed directly into CI/CD pipelines, enabling automated remediation. ➡️ Using jq The jq tool is specifically designed for processing JSON on the command line: # Compare after sorting keys jq --sort-keys . file1.json > sorted1.json jq --sort-keys . file2.json > sorted2.json diff sorted1.json sorted2.json Pros: Scriptable and automatable Works well in CI/CD pipelines Highly customizable Cons: Steeper learning curve Output can be verbose May require additional parsing for complex comparisons 3. Online JSON Comparison Tools Online tools provide visual, user-friendly ways to compare JSON structures. These are particularly helpful for team collaboration and sharing results. Top Online JSON Comparison Tools Tool Highlights HyperTest JSON Comparison Tool -Color-coded diff visualization -Structural analysis -Key-based comparison -Handles large JSON files efficiently JSONCompare - Side-by-side view - Syntax highlighting - Export options JSONDiff - Tree-based visualization - Change statistics CodeBeautify - Multiple formatting options - Built-in validation The HyperTest JSON Comparison Tool stands out particularly for its performance with large files and intuitive visual indicators that make complex structural differences immediately apparent. Let's look at an example of comparing two versions of a user profile with the HyperTest tool: Before: { "name": "John", "age": 25, "location": "New York", "hobbies": [ "Reading", "Cycling", "Hiking" ] } After: { "name": "John", "age": 26, "location": "San Francisco", "hobbies": [ "Reading", "Traveling" ], "job": "Software Developer" } Using the HyperTest JSON Comparison Tool , these differences would be immediately highlighted: Changed: age from 25 to 26 Changed: location from "New York" to "San Francisco" Modified array: hobbies (removed "Cycling", "Hiking"; added "Traveling") Added: job with value "Software Developer" Try the tool here Pros: Intuitive visual interface No installation required Easy to share results Great for non-technical stakeholders Cons: Requires internet connection May have file size limitations Potential privacy concerns with sensitive data NoSQL databases like MongoDB, which store data in JSON-like documents, have seen a 40% year-over-year growth in enterprise adoption. 4. Programming Languages and Libraries For integration into your development workflow, libraries in various programming languages offer JSON comparison capabilities. ➡️ Python Using the jsondiff library: from jsondiff import diff import json with open('file1.json') as f1, open('file2.json') as f2: json1 = json.load(f1) json2 = json.load(f2) differences = diff(json1, json2) print(differences) ➡️ JavaScript/Node.js Using the deep-object-diff package: const { diff } = require('deep-object-diff'); const fs = require('fs'); const file1 = JSON.parse(fs.readFileSync('file1.json')); const file2 = JSON.parse(fs.readFileSync('file2.json')); console.log(diff(file1, file2)); Pros: Fully customizable Can be integrated into existing workflows Supports complex comparison logic Can be extended with custom rules Cons: Requires programming knowledge May need additional work for visual representation Initial setup time 5. IDE Extensions and Plugins Many popular IDEs offer built-in or extension-based JSON comparison: IDE Extension/Feature VS Code Compare JSON extension JetBrains IDEs Built-in file comparison Sublime Text FileDiffs package Atom Compare Files package Pros: Integrated into development environment Works offline Usually supports syntax highlighting Cons: IDE-specific May lack advanced features Limited visualization options Advanced JSON Comparison Techniques ➡️ Semantic Comparison Sometimes you need to compare JSON files based on their meaning rather than exact structure. For example: // File 1 { "user": { "firstName": "John", "lastName": "Doe" } } // File 2 { "user": { "fullName": "John Doe" } } While structurally different, these might be semantically equivalent for your application. Custom scripts or specialized tools like the HyperTest JSON Comparison Tool offer options for rule-based comparison that can handle such cases. ➡️ Schema-Based Comparison Instead of comparing the entire JSON structure, you might only care about changes to specific fields or patterns: // Example schema-based comparison logic function compareBySchema(json1, json2, schema) { const result = {}; for (const field of schema.fields) { if (json1[field] !== json2[field]) { result[field] = { oldValue: json1[field], newValue: json2[field] }; } } return result; } Real-world use cases for JSON Comparison ➡️ API Response Validation When developing or testing APIs, comparing expected and actual responses helps ensure correct behavior: // Test case for user profile API test('should return correct user profile', async () => { const response = await api.getUserProfile(123); const expectedResponse = require('./fixtures/expectedProfile.json'); expect(deepEqual(response, expectedResponse)).toBe(true); }); ➡️ Configuration Management Tracking changes across environment configurations helps prevent deployment issues: # Script to check configuration differences between environments jq --sort-keys . dev-config.json > sorted-dev.json jq --sort-keys . prod-config.json > sorted-prod.json diff sorted-dev.json sorted-prod.json > config-diff.txt ➡️ Database Migration Verification Before and after snapshots ensure data integrity during migrations: # Python script to verify migration results import json from jsondiff import diff with open('pre_migration.json') as pre, open('post_migration.json') as post: pre_data = json.load(pre) post_data = json.load(post) differences = diff(pre_data, post_data) # Expected differences based on migration plan expected_changes = { 'schema_version': ('1.0', '2.0'), 'field_renamed': {'old_name': 'new_name'} } # Verify changes match expectations # ... Best Practices for JSON Comparison Normalize Before Comparing : Sort keys, standardize formatting, and handle whitespace consistently. Use Purpose-Built Tools : Choose comparison tools designed specifically for JSON rather than generic text comparison. Automate Routine Comparisons : Integrate comparison into CI/CD pipelines and testing frameworks. Consider Context : Sometimes structural equivalence matters; other times, semantic equivalence is more important. Document Expected Differences : When comparing across environments or versions, maintain a list of expected variances. Handle Large Files Efficiently : For very large JSON files, use streaming parsers or specialized tools like the HyperTest JSON Comparison Tool that can handle substantial files without performance issues. Future of JSON Comparison As JSON continues to dominate data interchange, comparison tools are evolving: AI-Assisted Comparison : Machine learning algorithms that understand semantic equivalence beyond structural matching. Real-time Collaborative Comparison : Team-based analysis with annotation and discussion features. Integration with Schema Registries : Comparison against standardized schemas for automatic validation. Performance Optimizations : Handling increasingly large JSON datasets efficiently. Cross-Format Comparison : Comparing JSON with other formats like YAML, XML, or Protobuf. Conclusion Effective JSON comparison is an essential skill for modern developers. From simple visual inspection to sophisticated programmatic analysis, the right approach depends on your specific requirements, team structure, and workflow integration needs. By leveraging tools like the HyperTest JSON Comparison Tool for visual analysis and integrating command-line utilities or programming libraries into your development process, you can catch JSON-related issues before they impact your users or systems. Try the Online JSON Comparison tool here Remember that the goal isn't just to identify differences but to understand their implications in your specific context. A minor JSON change might be inconsequential—or it might bring down your entire system. The right comparison strategy helps distinguish between the two. Related to Integration Testing Frequently Asked Questions 1. Why do developers need to compare JSON files? Developers compare JSON files to track changes, debug issues, validate API responses, manage configurations across environments, and ensure data integrity during transformations or migrations. 2. What are the challenges developers face when manually comparing JSON files? Manual comparison becomes challenging due to nested structures, formatting differences, key order variations, and the sheer volume of data in complex JSON files. Human error is also a significant factor. 4. What are the advantages of using online JSON diff tools? Online tools like HyperTest's JSON comparison provide visual, user-friendly interfaces with color-coded differences, side-by-side views, and specialized JSON understanding. For your next read Dive deeper with these related posts! 08 Min. Read Using Blue Green Deployment to Always be Release Ready Learn More 09 Min. Read CI/CD tools showdown: Is Jenkins still the best choice? Learn More 08 Min. Read How can engineering teams identify and fix flaky tests? Learn More

Unlock the full potential of Postman with our 2024 guide – your ultimate resource for mastering Postman's features and capabilities. 8 February 2024 13 Min. Read The Most Comprehensive ‘How to use’ Postman Guide for 2024 WhatsApp LinkedIn X (Twitter) Copy link Download the 101 Guide Welcome to this comprehensive tutorial on mastering Postman, the popular API testing tool . In this guide, we delve into the core functionalities of Postman, exploring its powerful features such as Postman Tests, Data Parameterization, Collections, and Data-Driven Testing. Whether you're a beginner stepping into the world of API development or an experienced developer seeking to enhance your testing strategies, this tutorial is designed to provide you with a deep understanding of how Postman can streamline your API testing process. We'll walk through practical examples, including working with the JSONPlaceholder API, to demonstrate how you can leverage Postman to create efficient, robust, and reusable tests, making your API development process both effective and scalable. We can start with absolute fundamentals by learning how to construct and test GET and POST requests with Postman. Let’s begin. Working with requests, GET and POST We will use https://jsonplaceholder.typicode.com/posts for our tutorial which is a fake online REST service, so it simulates the behavior of a real API but doesn't actually create or store data. ➡️GET Request in Postman A GET request is used to retrieve data from a server. Here’s how you can make a GET request to the JSONPlaceholder API to fetch posts: 1.Open Postman : Start by opening Postman on your computer. 2. Create a New Request : Click on the “New” button or the "+" tab to open a new request tab. 3. Set the HTTP Method to GET : On the new request tab, you will see a dropdown menu next to the URL field. Select "GET" from this dropdown. 4. Enter the Request URL : In the URL field, enter the endpoint for fetching posts: https://jsonplaceholder.typicode.com/posts . This URL is the endpoint provided by JSONPlaceholder to get a list of posts. 5. Send the Request : Click the "Send" button to make the request. 6. View the Response : The response from the server will be displayed in the lower section of Postman. It should show a list of posts in JSON format. ➡️POST Request in Postman A POST request is used to send data to a server to create/update a resource. Here’s how to make a POST request to the JSONPlaceholder API to create a new post: 1.Create a New Request : As before, open a new request tab in Postman. 2. Set the HTTP Method to POST : Select "POST" from the dropdown menu next to the URL field. 3. Enter the Request URL : Use the same URL as the GET request: https://jsonplaceholder.typicode.com/posts . 4. Enter Request Headers : Go to the "Headers" tab in the request setup. Add a header with Key as "Content-Type" and Value as "application/json". This indicates that the body of your request is in JSON format. 5. Enter Request Body : Switch to the "Body" tab. Select the "raw" radio button and choose "JSON" from the dropdown. Enter the JSON data for the new post. For example: jsonCopy code { "title": "foo", "body": "bar", "userId": 1 } 6. Send the Request : Click the "Send" button. 7. View the Response : The server's response will be displayed in the lower section. For JSONPlaceholder, it will show the JSON data for the newly created post, including a new ID. Creating Tests using Postman A Postman test is a set of instructions written in JavaScript that are executed after a Postman request is sent to validate various aspects of the response. These tests are used to ensure that the API behaves as expected. They can check for aspects such as the status code, response time, the structure of the response data, and the correctness of response values. Let's use the JSONPlaceholder API (a fake online REST API) as an example to explain how to write and execute tests in Postman. Example: Testing the JSONPlaceholder API Suppose we're testing the /posts endpoint of the JSONPlaceholder API, which returns a list of posts. 1. Creating a Test for Checking Response Status Goal : To ensure the request to the /posts endpoint returns a successful response. Test Setup : Send a GET request to https://jsonplaceholder.typicode.com/posts . In the "Tests" tab in Postman, write a JavaScript test to check if the response status code is 200 (OK). Example script: pm.test("Status code is 200", function () { pm.response.to.have.status(200); }); 2. Testing Response Structure Goal : To validate that the response is an array and each item in the array has certain properties (like userId , id , title , body ). Test Setup : After sending the GET request, write a test to check the structure. Example script: pm.test("Response must be an array and have required properties", function () { let jsonData = pm.response.json(); pm.expect(Array.isArray(jsonData)).to.be.true; jsonData.forEach((item) => { pm.expect(item).to.have.all.keys('userId', 'id', 'title', 'body'); }); }); 3. Checking Response Content Goal : To verify that the response contains posts with correct data types for each field. Test Setup : Write a test to validate data types. Example script: pm.test("Data types are correct", function () { let jsonData = pm.response.json(); jsonData.forEach((item) => { pm.expect(item.userId).to.be.a('number'); pm.expect(item.id).to.be.a('number'); pm.expect(item.title).to.be.a('string'); pm.expect(item.body).to.be.a('string'); }); }); So good Postman tests need to have good assertions that check for status codes, schema and the data. These JS code blocks need to be written and updated with every minor or major change in the API to keep testing the updated reality. We understand that’s a lot of manual work, and the fast-moving agile teams can’t keep up with their release cycles if they are stuck in this process of building test cases manually. That’s why we at HyperTest have created an approach that automatically works on building API tests and writing assertions. The SDK version of HyperTest sits in your code and monitors the application to auto-generate high-level unit tests that test every commit. It’s record and replay mode is capable of covering every possible user scenario, eliminating the need to write and maintain test cases on your own. The HyperTest SDK is positioned directly above a service or SUT, where it monitors and logs telemetry data of all incoming requests, responses of the SUT and its dependent systems. Covers more scenarios than humanly possible, and when replayed it verifies the SUT and its communication with all dependencies without asking teams to write a single line of test code. 📢 Curious on knowing more capabilities of HyperTest? Let’s get you started! If you possess an abundance of time and are amenable to dedicating days to the writing and upkeep of test cases, as opposed to the mere minutes required with HyperTest , then let us proceed with the tutorial. Data Parametrization to make Postman Tests Reusable Request parameterisation in Postman allows you to define variables that can be used across multiple requests. This is particularly useful for testing different scenarios or for reusing similar requests with different data. We'll continue using the JSONPlaceholder API for this example. Step-by-Step Guide for Request Parameterisation in Postman 1. Setting Up Environment Variables 1.1. Create an Environment : First, you need to create an environment in Postman. Click on the “Environments” tab on the left sidebar. Click on “New” to create a new environment. Name it something relevant, like “TestEnv”. 1.2. Add Variables : In your new environment, add variables that you want to parameterise. For example, you can create variables like baseUrl and userId . Set the initial value for baseUrl as https://jsonplaceholder.typicode.com and for userId as 1 . 1.3. Select the Environment : Once you've set up your environment, select it from the dropdown at the top right corner of Postman. 2. Using Variables in Requests 2.1. Create a GET Request : Let’s say you want to fetch posts of a specific user. Create a new request by clicking on the "+" tab. 2.2. Set Up the GET Request with Variables : In the URL field, use the variables by wrapping them in double curly braces. For example, enter {{baseUrl}}/posts?userId={{userId}} . This tells Postman to replace these placeholders with the corresponding variable values from the selected environment. 2.3. Send the Request : Click “Send” and observe how Postman substitutes the variables with their actual values and executes the request. 3. Changing Variable Values Edit Variables : Go back to your environment settings. Change the value of userId to another number, like 2 . Resend the Request : With the environment still selected, resend the same request. Notice how the request now fetches posts for the updated user ID. 4. Using Variables in POST Request Create a POST Request : Open a new tab and set the request type to POST. For the URL, use {{baseUrl}}/posts . Setup Headers : Set the “Content-Type” header to “application/json”. Setup Body with Variables : In the request body (raw JSON format), you can also use variables. For example: { "title": "A Title", "body": "Post body", "userId": {{userId}} } Postman can also generate random data for your requests without the need for you to prepare a dataset yourself. This is typically done using dynamic variables in Postman's scripting feature. Postman has a built-in dynamic variable feature and scripting capabilities in the Pre-request Script and Tests sections, where you can use JavaScript to generate random data. ➡️Using Built-in Dynamic Variables Postman offers a set of dynamic variables that you can use directly in your requests. For example: {{$randomInt}} : A random integer between 0 and 1000. {{$guid}} : A v4 style GUID. {{$timestamp}} : The current UNIX timestamp. {{$randomFirstName}} : A random first name. You can use these directly in your URL, query parameters, headers, or body. For example, if you need a random email, you could set up your JSON body like this: { "email": "user{{$randomInt}}@example.com", "name": "{{$randomFirstName}}" } ➡️Using Pre-request Scripts for Custom Random Data For more specific random data needs, you can write JavaScript code in the Pre-request Script tab of your request. Here's an example: javascriptCopy code // Generate a random user ID between 1 and 100 pm.environment.set("userId", Math.floor(Math.random() * 100) + 1); // Generate a random username var usernames = ['userA', 'userB', 'userC', 'userD']; pm.environment.set("userName", usernames[Math.floor(Math.random() * usernames.length)]); Then, in your request, you can use {{userId}} and {{userName}} as variables, and they will be replaced with the values set in the script. Creating test data for data-driven testing is widely recognized as a significant challenge by QA professionals. HyperTest's mocking capabilities entirely remove the difficulty of maintaining states for testing specific features. Let's shed some light on this: Imagine you’re testing an e-commerce app. There’s this new feature of “loyalty points” you want to test. But before getting to that stage, you need to 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. HyperTest has developed an approach to help quality teams test end-to-end scenarios without needing to spend any time creating and managing test data. Interested in data driven testing without needing data preparation? ➡️Using Tests to Assert Random Responses Similarly, if you want to validate the response of a request that returns random data, you can write scripts in the Tests tab. For example, to check if a returned ID is an integer: javascriptCopy code var jsonData = pm.response.json(); pm.test("ID is an integer", function () { pm.expect(Number.isInteger(jsonData.id)).to.be.true; }); Postman Collections A Postman Collection is a group of saved API requests that can be organized into folders. Collections are useful for grouping together related API requests, which can be for the same API or a set of APIs that serve a similar function. Collections in Postman can also contain subfolders, environments, tests, and scripts, providing a comprehensive suite for API testing and development. 1. Creating the Collection Start by Creating a New Collection : In Postman, click on the “New” button, then choose “Collection”. Name the collection, for example, “JSONPlaceholder API Tests”. Add Descriptions (Optional) : You can add a description to your collection, which can include notes about the API, its usage, or any other relevant information. 2. Adding Requests to the Collection Create Requests for Various Endpoints : Within this collection, you can create different API requests corresponding to the various endpoints of the JSONPlaceholder API. For example: A GET request to /posts to retrieve all posts. A POST request to /posts to create a new post. A GET request to /posts/{id} to retrieve a specific post by ID. A PUT request to /posts/{id} to update a specific post. A DELETE request to /posts/{id} to delete a specific post. Organizing with Folders : For better organization, you can create folders within the collection. For instance, separate folders for “Posts”, “Comments”, “Users”, etc., if you plan to expand testing to cover these areas. 3. Adding Tests and Scripts Each request in the collection can have its own set of tests and pre-request scripts. This allows you to automate testing and set up environments dynamically. For instance, you might write tests to validate the response structure and status code for each GET request. 4. Using Environments with the Collection You can create and select different environments for your collection. For example, you might have a “Testing” environment with the base URL set to the JSONPlaceholder API. This environment can then be used across all requests in the collection. 5. Sharing and Collaboration Collections can be shared with team members or exported for use by other stakeholders. This is particularly useful for collaborative projects and ensures consistency across different development environments. 6. Running Collections Postman also allows you to run the entire collection or specific folders within a collection. This is useful for regression testing , where you need to verify that your API behaves as expected after changes. Interested in an approach that can automatically generate Postman collections without writing scripts? Data Driven Testing Now that you understand Postman collections, you’d be interested in a Postman capability that lets you test your APIs with different data sets. This helps you verify if the APIs in a test scenario behave the same way with different data without manually changing the input for each test. Example: Data-Driven Testing with JSONPlaceholder API Scenario: Testing User Posts Creation Suppose you want to test the creation of posts for different users and ensure that the API correctly processes various input data. You would need to test the POST request to the /posts endpoint with different sets of user data. Step 1: Prepare the Data File First, you need to create a data file in JSON or CSV format. This file should contain the data sets you want to test. Here's an example JSON data file: [ {"userId": 1, "title": "Post 1", "body": "Body of post 1"}, {"userId": 2, "title": "Post 2", "body": "Body of post 2"}, {"userId": 3, "title": "Post 3", "body": "Body of post 3"} ] Each object in the array represents a different set of data to be used in the test. Step 2: Create a POST Request in Postman Create a new request in Postman to POST data to https://jsonplaceholder.typicode.com/posts . In the request body, use variables to represent data that will be taken from your data file. For example: { "userId": {{userId}}, "title": "{{title}}", "body": "{{body}}" } Step 3: Write Tests for Your Request In the "Tests" tab of the request, you can write tests to validate the response for each data set. For example: pm.test("Response has correct userId", function () { var responseJson = pm.response.json(); pm.expect(responseJson.userId).to.eql(parseInt(pm.variables.get("userId"))); }); This test checks if the userId in the response matches the userId sent in the request. Step 4: Running the Collection with the Data File ➡️ Save your request into a collection. ➡️ To run the collection with your data file, click on the Runner button in Postman. ➡️ Drag and drop your collection into the Runner, and then drag and drop your data file into the "Select File" area under the "Data" section. ➡️ Run the collection. Postman will execute the request once for each set of data in your data file. Step 5: Analyze the Test Results ➡️ In the Runner, you'll see the results of each test for every iteration (each set of data from your file). ➡️ Review the results to ensure your API handles each data set as expected. Mocking: Powerful way to simulate and test APIs Mocking is a trick that is routinely employed by developers and testers to continue to test APIs that depend on external services or 3rd party APIs, without needing these dependencies available all the time. Say you are developer that needs to test a new feature and in order to do that successfully you call and consume the response of any external, 3rd party or internal API. Mocking allows you to keep a dummy response of the dependency ready as when you run a collection or Postman test that runs your API. This also helps when you want to test interactions with APIs that you don't control or when you're trying to avoid costs, rate limits, or side effects of using the real API. Example Scenario: Mocking a Weather API Scenario Description Suppose your application needs to fetch weather information from a third-party weather API. The actual API, let's call it RealWeatherAPI , provides weather data based on location. However, you want to mock this API for testing purposes. Step 1: Define the API Request Identify the API Request : Determine the structure of the request you need to make. For example, a GET request to https://realweatherapi.com/data?location=London to fetch weather data for London. Step 2: Create a Mock Request in Postman Open Postman and create a new request. Set Up the Request : Use the method and URL pattern of the RealWeatherAPI . For example, set the method to GET and URL to something like https://{{mockServer}}/data?location=London . Step 3: Define a Sample Response Add a New Example : In the request setup, go to the "Examples" section and create a new example. Mock the Response : Set the response status to 200 OK. Define a mock response body that resembles what you'd expect from RealWeatherAPI . For example: { "location": "London", "temperature": "15°C", "condition": "Partly Cloudy" } Step 4: Create a Mock Server Navigate to the 'Mocks' Tab and create a new mock server. Configure the Mock Server : Choose the request you created. Give your mock server a name, like "Weather API Mock". Create the mock server and copy its URL. Step 5: Use the Mock Server Update the Request URL : Replace {{mockServer}} in your request URL with the actual mock server URL provided by Postman. Send the Request : When you send the request, you should receive the mocked response you defined earlier. Step 6: Integrate Mock Server in Your Application Use the Mock Server URL : In your application code, replace calls to RealWeatherAPI with calls to your Postman mock server. Test Your Application : Test your application's functionality using the mock responses to ensure it handles the data correctly. Interested in testing with mocks without having to write and maintain mocks? Conclusion We conclude the article here, having focused specifically on topics that are relevant for beginner to intermediate level developers and API testers who want to consider Postman for API development, management and testing. Postman stands out not just as a tool for making API calls, but as a complete suite for API development, testing, documentation, and collaboration. Its ability to simulate various scenarios, automate tests, and integrate seamlessly into different stages of API lifecycle management makes it an indispensable asset for developers and testers alike. If you've reached this point and appreciate Postman's capabilities, take a look at HyperTest . It effortlessly manages tasks, automatically crafting and executing API tests, conducting data-driven testing without any data preparation, and handling advanced features like API mocking and environment management. The no-code capability of our solution has empowered teams at companies such as Nykaa, Fyers, Porter, etc., leading to a remarkable 50% reduction in testing time and a substantial 90% enhancement in code quality. See it in action now! Frequently Asked Questions 1. What are the basics of Postman? Postman is a popular tool for API testing that allows users to send requests to web servers and view responses. It supports various HTTP methods like GET, POST, and PUT. Users can add headers, parameters, and body data to requests. Postman facilitates the organization of tests into collections and offers features for automated testing, environment variables, and response validation, streamlining API development and testing workflows. 2. How to use Postman tool for API testing? To use Postman for API testing, install Postman, create a new request, and select the HTTP method. Enter the API endpoint, add headers or parameters if needed, and input body data for methods like POST. Click "Send" to execute the request and analyze the response. Requests can be organized into collections for management. Postman also supports variables and tests for dynamic data and response validation. 3. What are the different request types supported by Postman? Postman supports several HTTP request types, including GET (retrieve data), POST (submit data), PUT (update resources), DELETE (remove resources), PATCH (make partial updates), HEAD (retrieve headers), OPTIONS (get supported HTTP methods), and more, catering to diverse API testing needs. For your next read Dive deeper with these related posts! 04 Min. Read Postman Tool for API Testing Vs HyperTest: Comparison Learn More 11 Min. Read Top 5 Katalon Alternatives and Competitors Learn More 07 Min. Read FinTech Regression Testing Essentials Learn More

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