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HyperTest generates integration tests that achieve over 90% coverage, ensuring fast and bug-free deployment of distributed services. WEBINAR | On-Demand | "No More Writing Mocks: The Future of Unit & Integration Testing" >> HyperTest enables developers to fix integration issues quickly Use AI agents that tests every possible scenario pre-release so that developers release on-time, without worrying about failures Request a Demo Product Live Tour Developers at the most innovative companies trust HyperTest for confident releases Why we built HyperTest? Unit tests are useful for checking the logic within a service but fail to test the dependencies between services. Integration testing comes to the rescue, but as opposed to the well-standardized unit testing frameworks, there was no off-the-shelf integration testing framework that we could use for our back-end services. Paul Marinescu Research Scientist View Source How it Works For Developers For Engineering Leaders 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. Hear from our Customers HyperTest has been a game-changer for us in Integration testing. It has significantly saved time and effort by green-lighting changes before they go live with our weekly releases. Vinay Jaasti Chief Technology Officer We have recently upgraded our code framework. And by running one instance of Hypertest, we got the first-cut errors in less than an hour , which could have taken us a few days. Vibhor G VP of Engineering Hypertest unique selling point is its ability to generate tests by capturing network traffic, they have reduced the overhead of writing test cases, and its reports and integrations have helped us smoke out bugs very quickly with very little manual intervention. Ajay Srinivasan Senior Technical Lead Trace failing requests across microservices Test Service Mesh with Distributed Tracing HyperTest context propagation provides traces across multiple microservices, helping developers debug root causes in a single view. It cuts debugging time and tracks data flow between services, showing the entire chain of events leading to failure. Read More Test code, APIs, data, queues without writing tests Power of foundational models with Record and Replay Test workflows, data and schema across APIs, database calls and message queues. Generate tests from real userflows to uncover problems that only appear in production like environments Read More Shift-left with your CI pipeline Release with High Coverage without writing tests Forget writing unit tests and measure all tested and untested parts of your code. Cover legacy to new code in days. Read More Top Use Cases From APIs to Queues, Databases to Microservices: Master Your Integrations High Unit Test Coverage HyperTest can help you achieve high >90% of code coverage autonomously and at scale. We can write 365 days of effort in less than a few hours. Database Integrations It can test the integration between your application and its databases, ensuring data consistency, accuracy, and proper handling of database transactions. API Testing HyperTest can validate the interactions between different components of your application through API testing. It ensures that APIs are functioning correctly and communicate seamlessly. Message Queue Testing If your application relies on message queues for communication, HyperTest can verify the correct sending, receiving, and processing of messages. Microservices Testing HyperTest is designed to handle the complexities of testing microservices, ensuring that these independently deployable services work harmoniously together. 3rd-Party Service Testing It can test the integration with external services and APIs, ensuring that your application can effectively communicate with third-party providers. HyperTest in Numbers 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 Book a Demo

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June 2024 Edition: Why E2E Fails, Message Queues Testing Guide, Upcoming Events & More for Engineering Leaders Hey there! 👋 Remember those awesome engineering books we recommended? We hope you enjoyed them! Speaking of awesome, Bug-Free Bytes Vol. 6 is here already! Get ready to dive in. a. End-to-End Message Queue Testing for a Better Delivery Kafka, RabbitMQ, and Amazon SQS are popular choices, but pinpointing the culprit – the queue, producer, or consumer – becomes a frustrating guessing game when issues arise. This whitepaper's smart testing techniques get clear insights into how your A, B, C (and beyond!) services communicate. Access the whitepaper here! b. This Month's Community Picks: 👉 Login to smooth sailing, or log out in frustration? Unveiling the truth about End-to-End testing – is it your software's heaven or heck? Learn more 👉 Wondering why your website's slower than a dial-up connection? HTTP2 might just be your solution to achieve speed. Learn more 👉 Imagine catching bugs before they cause problems - that's developer confidence at its finest! Discover more about this proactive practice here. Learn more c. Upcoming Events: 🧭 Dive into the Future of Web: Browser Conference 2024 (Online): Browser Conference is a leading event dedicated to exploring the latest advancements and innovations shaping the future of the web. 🧭 Actionable insights on Generative AI, security, modern web apps, and more (Online): Get clarity from senior software practitioners on today's critical dev priorities with two days of technical talks and peer connections. 🧭 It’s ChatGPT time again (28 June, Munich/ Online): This session at the Machine Learning Technologies & Innovations Conference (ML-Con) will offer an in-depth exploration of ChatGPT, a powerful AI tool beloved by many. Please share your feedback; your input helps us keep making it even better. 🎉 Cheers, Team HyperTest 💙

Online Postman Tool For API Testing VS HyperTest - Visit us and check out which one is more powerful when it comes to catching API failures. 14 February 2022 04 Min. Read Postman Tool for API Testing Vs HyperTest: Comparison WhatsApp LinkedIn X (Twitter) Copy link Get the Guide Every company developing a product should have tests in place. Just like it’s important to have tests in place, it also matters how we run these tests. An increasing number of such companies are resorting to testing automation to ship out a bug-free product faster. While Postman has long been a staple in the API testing community, HyperTest offers advanced features that make it a compelling alternative. In this blog, we will attempt to draw a comparison between Postman online and HyperTest to try and understand which is more powerful when it comes to catching API failures . Imagine a sequence of 10 API requests, one after the other that make up your test case. Postman HyperTest On Postman online, you would have to build something called a collection where you put these API requests in the right sequence and with the right data. Whereas on HyperTest, you can have this collection or sequence auto-generated (with the data) just from the network traffic. Why should you consider moving out from Postman? ➡️ Writing your own tests on Postman Online comes with the added responsibility of maintenance . What this means is that every small to large change will need all hand-written tests to be updated on Postman Online for each time and every sprint. ➡️ In a fast-paced environment, code changes happen every day because the codebase is very agile. This means a disproportionate increase in maintenance effort because even a single line of code change could require multiple lines of an update on your postman Online tests. Feature/Aspect Postman HyperTest What does it do? API testing & documentation Complete backend testing : Test APIs, database calls, message queues & inter-service contracts What will it not do? Will not test database calls, async flows or message queues Front-end testing: It will not test UI or cross browser testing Who are its users? Developers, SDETs, test engineers and QAs Only Developers ➡️ If writing tests was not hard enough, how about assertions. The biggest problem with hand-written tests on Postman Online is to think about which business rules to assert and if you have covered them all. Phew, this needs a genius! 💡 We would not miss any API being tested as HyperTest ensures all the requests are replayed from network traffic while running a test. -Sunil Kumar, Yellow.ai ➡️ Teams that are working with Postman Online, split their time thinking about all the flows that can be automated, the assertions within them and still worry if they have covered every business rule inside and every possible workflow with these tests. ➡️ Tests automation built with Postman Online neither gives you the complete confidence of never missing bugs to production nor reduces your effort on test maintenance. ➡️ Moreover, Postman Online tool users are unable to reuse their pre-written scripts or add more requests because of low script reusability. This means testers have to create new test scripts over and over for each project. ➡️ The nature of Postman becomes an impediment to cross-functional collaboration in Agile teams where every member must keep close communication with each other. Feature/Aspect With Postman With HyperTest How does it work? Write API tests manually on Postman to test HTTP requests and responses Record and Replay: Generates APIs tests automatically from real user traffic. 100% autonomous. Where are the tests run? Using Postman runners and Newman (CLI) on Postman cloud No dedicated or isolated environments needed. These tests can be run locally 100% on-prem How does one start? Create API request in Postman and write validations on responses. Build collections by stacking API tests manually 10-line SDK in the source code of repo. Records traffic and builds tests which can be replayed later using CLI Why HyperTest is a Better Alternative? HyperTest will auto-generate an end-to-end test case that you have to otherwise put together one by one in Postman online. The maintenance on HyperTest generated tests is ZERO. Not only does it generate a request-by-request collection like Postman online in seconds, but these tests are also updated by themselves without any manual input. We can automate our complete service regression testing with it. It saves a lot of developer time from testing and increases our confidence in making the change live. -Shashank Sah, Urban Company Feature/Aspect With Postman With HyperTest Scope Limited. Just testing APIs Comprehensive. APIs, database calls, queue producers & consumers Maintenance Manual. Tests scripts need to be written and updated manually. Requires regular maintenance of test cases as APIs change 100% autonomous or no-code. Automatically generates API tests by recording actual user flows. Auto updates all test cases and assertions as APIs change Quality of Tests Poor. Depends on quality of assertions which are manually written High. Quality programmatically generated assertions that cover schema & data to never miss errors Test Data Management Yes. set pre-request scripts to seed and update test data before Postman tests are run No. HyperTest uses data from traffic for tests and keeps it reusable. Handles both read & write requests Test databases? No Yes Test message queues? No Yes Test Coverage Unknown. No way to measure test coverage Measurable. Reports code coverage i.e. actual lines of code that are tested Test Execution Speed High. Depends on response time of API and environment (db etc) Very Less. Runs as fast as unit tests. Does not need dedicated environments And the best part about HyperTest is zero effort on assertions . HyperTest can generate assertions dynamically at the run-time i.e. time of execution by comparing your test version with the stable version. It asserts everything on the response, without any moderation, and intelligently figures out which of the breaking assertions can become errors and which are benign. No headache or worry thinking about assertions ever. How cool! Why Should You Consider Moving from Postman? 1. High Quality Automation Postman: While Postman supports automated testing through collections and integrations with CI/CD pipelines, the automation capabilities can be somewhat limited, particularly for complex testing scenarios. HyperTest: HyperTest excels in automation, providing more granular control over test execution and integration with various CI/CD tools. It is 100% autonomous in nature, automatically generates API tests by recording actual user flows and auto updates all test cases and assertions as APIs change with zero manual interference. 2. High >90% of Code Coverage at scale Postman: Postman does not provide built-in mechanisms to calculate code coverage percentages or identify untested code paths. Managing and correlating test results with code changes across large teams can be cumbersome without any clear insights on code coverage. HyperTest: HyperTest generates a code coverage report after every run. This highlights clearly how it tests code paths for both the data and integration layer along with the core logic. 3. Distributed Tracing Postman: It lacks built-in support for distributed tracing, which is crucial for understanding and diagnosing issues in complex, microservices-based architectures. HyperTest: It offers distributed tracing capabilities, which allow developers to trace requests across multiple services and understand the full journey of a request through their system. This helps in identifying performance bottlenecks, diagnosing issues, and gaining better insights into the interactions between different services. 💡 HyperTest  provides distributed tracing, offering end-to-end visibility across microservices, helping developers debug root causes of failures in a single view. 4. Smart Mocking Postman: Provides manual mocking through examples and mock servers, requiring significant setup and maintenance. It lacks automatic updates based on dependency changes. HyperTest uses smart mocking for external systems like databases, queues, and third-party APIs, automatically refreshing mocks as dependencies change, keeping tests non-flaky and consistent. 5. Reduce Time Spent Writing Test Code by 95% Postman: Requires significant manual effort to create and maintain test scripts, often leading to prolonged test development cycles and reduced efficiency. HyperTest: Automates test creation from recorded user interactions, drastically reducing time spent on scripting. This accelerated development process allows teams to focus on core product features rather than test maintenance. 💡It is helping us in ensuring that the new commit is not affecting any of the existing APIs in terms of performance or business logic. Due to this, the amount of time needed to write test cases or have them manually tested every time is avoided. -Arun Aranganathan, Fleek Technologies Summary To wrap up, HyperTest needs little to no maintenance vs Postman Online tests. HyperTest can cover the application more extensively through network traffic as compared to Postman Online and ends up detecting more bugs through dynamic assertions that are deep and wide. So, in a nutshell, migrating from Postman online to HyperTest for all your API automation needs can mean: Spending way less time building or maintaining test automation Catching way more bugs or issues without ever writing a single line of code Sleeping like a baby before every release, knowing no major error can ever leak into production If you’re interested in learning more, schedule a call with an implementation specialist to see how HyperTest can help you ship a bug-free product. Frequently Asked Questions 1. What is postman online? Postman online is a web-based platform for building and using APIs. It lets you send requests, test functionality, and collaborate with others 2. Can I use Postman online? Yes, you can use Postman Online by signing up on the website and accessing your workspace through a web browser. 3. How to test Postman online? No need for setup! Just sign up for Postman online and start sending requests. Test APIs with ease, build collections, and collaborate with your team. For your next read Dive deeper with these related posts! 13 Min. Read The Most Comprehensive ‘How to use’ Postman Guide for 2024 Learn More 10 Min. Read Top 10 API Testing Tools in 2025: A Complete Guide Learn More 11 Min. Read Top 5 Katalon Alternatives and Competitors Learn More

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Discover the importance of code coverage in testing and how it helps ensure thorough testing of software code for improved quality and reliability. 27 March 2024 07 Min. Read The Developer’s Handbook to Code Coverage WhatsApp LinkedIn X (Twitter) Copy link Checklist for best practices With software development constantly evolving, the importance of maintaining application quality and reliability cannot be overstated. Testing is a fundamental aspect of achieving this, with Code Coverage serving as a crucial metric. This blog post explores code coverage in testing, covering its significance, types, measurement methods, and recommended implementation strategies. Understanding Code Coverage Code coverage is a metric used to measure the extent to which the source code of a program is executed when a particular test suite runs. It's a quantitative measure that provides insights into the quality and effectiveness of your testing strategy. The fundamental goal of code coverage is to identify untested parts of the codebase, ensuring that the software is thoroughly tested, which in turn reduces the likelihood of bugs or issues in the production environment. 💡 Achieve more than 90% of code coverage with HyperTest, see it working here Why Is Code Coverage Important? The importance of code coverage in software testing can be summarized in the following points: Identifies Untested Code : It highlights areas of the code that have not been tested, helping developers to focus their testing efforts on those sections. Improves Code Quality : By striving for higher coverage, developers are encouraged to write more tests, leading to better code quality and reliability. 💡 See how Fyers achieved over 90% code coverage without writing a single line of code, improving code quality and developer productivity effectively. Facilitates Refactoring : Knowing which parts of the code are covered by tests gives developers the confidence to refactor code, improving its maintainability and performance without the fear of breaking existing functionality. Detects Dead Code : It helps in identifying unused or obsolete code that can be removed, simplifying the codebase and reducing maintenance overhead. Types of Code Coverage There are several types of code coverage, each focusing on different aspects of the code. The most common types include: Statement Coverage : Measures the percentage of code statements that are executed. Branch Coverage : Also known as decision coverage, it measures whether both the true and false branches of conditional statements have been executed. Function Coverage : Measures the percentage of functions or methods that are called during the test execution. Condition Coverage : Assesses whether every Boolean sub-expression evaluated both to true and false. Line Coverage : Similar to statement coverage but measured based on lines of code executed. Path Coverage : Aims to execute all possible paths through the code, including loops and conditional paths. Examples of Code Coverage in Practice To illustrate the concept of code coverage, let's consider a simple function that determines if a number is positive, negative, or zero: pythonCopy code def classify_number(num): if num > 0: return 'positive' elif num < 0: return 'negative' else: return 'zero' ✅Scenario 1: Basic Test Case Imagine we write a test case that only checks if the function correctly identifies a positive number: pythonCopy code def test_classify_positive(): assert classify_number(10) == 'positive' This test case would achieve 100% statement coverage if our code consisted solely of the classify_number function. However, it would only cover one of the three branches, resulting in 33% branch coverage. Tools for Measuring Code Coverage Several tools can be used to measure code coverage across different programming languages. But before we dive into each of those tools, let us share HyperTest with you, which works for Java as well as your Node apps. HyperTest : Get Real Code Coverage with HyperTest HyperTest takes the guesswork out of code coverage by recording actual requests, responses, and outbound calls from any environment. It then maps these interactions to the exact lines of code they execute, providing a precise coverage percentage right on your dashboard. No estimations-just real, actionable insights into how much of your code is actually tested. See it in action here: https://youtu.be/XaxbSFBhQu4?si=clWRkKxMOLWJiCfu Now coming back to other tools, let's start with Java: 1. Java JaCoCo (Java Code Coverage Library) : JaCoCo is a widely used open-source toolkit for measuring and reporting Java code coverage. It integrates seamlessly with build tools like Maven and Gradle, making it easy to include in CI/CD pipelines. JaCoCo provides detailed coverage reports, highlighting lines covered, branches taken, and complexity metrics. Cobertura : Another popular tool for Java applications, Cobertura calculates the percentage of code accessed by tests. It can generate reports in HTML or XML format, making it easier for developers to pinpoint untested parts of their codebase. Cobertura also measures line and branch coverage and can be used to identify areas of code that are not being tested effectively. 2. Python Coverage.py : This is the go-to tool for measuring code coverage in Python projects. It's compatible with Python versions 2.7 and 3.5 and newer. It can track code execution and provide reports on which parts of the code have been executed by tests. It supports a variety of report formats, including HTML, XML, and command-line output, and can be integrated with other tools and services, such as Coveralls and Codecov, for more comprehensive code quality analysis. 3. JavaScript Istanbul (nyc) : Istanbul is a JavaScript test coverage tool that works seamlessly with testing frameworks like Mocha and Jest. It instruments your JavaScript code on the fly, tracking which lines, functions, and branches are executed during testing. Istanbul generates detailed coverage reports in HTML, lcov, text, and JSON formats. The tool is capable of handling ES2015 and beyond, thanks to its use of Babel. Integrating Code Coverage Tools into Development Workflows Integrating code coverage tools into development workflows is straightforward with modern CI/CD pipelines. Most tools offer command-line interfaces that can be invoked during automated builds and testing stages. Here are some integration tips: Automate Coverage Reporting : Configure your CI/CD pipeline to automatically run code coverage analysis on each build. This ensures that code coverage data is always up-to-date and reflects the latest changes in the codebase. Set Coverage Goals : Establish coverage thresholds or goals for your project. Some tools allow you to fail the build if the coverage falls below a certain percentage, ensuring that test coverage remains a priority. Review Coverage Reports Regularly : Make it a habit to review coverage reports as part of your code review process. This helps identify gaps in the test coverage and areas of the code that may need additional tests. By leveraging these tools and integrating them effectively into your development processes, you can ensure that your codebase remains well-tested, reducing the likelihood of bugs and improving the overall quality of your software. Measuring Code Coverage Measuring code coverage involves using specialized tools that monitor and report the code executed during a test run. These tools typically instrument the codebase or monitor the execution environment to track which parts of the code are being exercised by the tests. After the tests are run, the tool generates a report detailing the coverage metrics, often highlighting the covered and uncovered sections of code. Some popular code coverage tools include HyperTest for Java and Node, JaCoCo for Java, Istanbul for JavaScript, and Coverage.py for Python. Best Practices for Implementing Code Coverage While code coverage is an invaluable metric for improving the quality of software testing , it's important to implement it thoughtfully. Here are some best practices: Aim for Realistic Coverage Goals : While achieving 100% code coverage might seem ideal, it's often not practical or necessary. Focus on critical parts of the application and set realistic coverage goals. Don't Sacrifice Test Quality for Coverage : High coverage with poorly written tests can be misleading. Ensure that tests are meaningful and effectively validate the code's behavior. Integrate with Continuous Integration (CI) : Automate code coverage reporting as part of your CI pipeline to regularly monitor and address coverage gaps. 💡 HyperTest integrates with majority of CI/CD tools making your development cycle easier and faster. See the working approach here. Use Coverage Reports to Guide Testing Efforts : Regularly review coverage reports to identify untested code and prioritize testing efforts accordingly. Combine with Other Quality Metrics : Code coverage should be one of several quality metrics you use to assess and improve your software. Conclusion Code coverage is a powerful metric that, when used correctly, can significantly enhance the quality and reliability of software by ensuring that code is thoroughly tested. By understanding the different types of coverage, measuring it accurately, and following best practices for its implementation, development teams can leverage code coverage to identify weaknesses in their tests and make informed decisions about where to focus their testing efforts. Remember, the ultimate goal is not to achieve 100% coverage, but to use code coverage as a tool to guide and improve the testing process, thereby increasing the overall quality of the software. Get to higher code coverage with HyperTest. Related to Integration Testing Frequently Asked Questions 1. Why is code coverage good? Code coverage is beneficial as it measures the proportion of code executed during testing, indicating how thoroughly the codebase has been tested. 2. What are the types of system testing? Code coverage helps identify untested code areas, enhancing test completeness. It also aids in improving code quality by highlighting areas for additional testing or refactoring. 3. What are the code coverage methods? Common code coverage methods include statement coverage (testing each line of code), branch coverage (testing all possible branches), and path coverage (testing every possible path through the code). For your next read Dive deeper with these related posts! 07 Min. Read Shift Left Testing: Types, Benefits and Challenges Learn More 11 Min. Read What is Software Testing? A Complete Guide Learn More Add a Title What is Integration Testing? A complete guide Learn More

HyperTest generates end-to-end API tests that achieve over 90% coverage, ensuring fast and bug-free deployment of distributed services. WEBINAR | On-Demand | "No More Writing Mocks: The Future of Unit & Integration Testing" >> Don’t Write API Tests in 2024 Automate Everything Why spend hours manually creating API tests? Choose HyperTest to handle end-to-end API testing, mocking and contract testing — no scripts, no hassle Get Started - It's Free Request a Demo Developers at the most innovative companies trust HyperTest for confident releases What are your options? Postman | Katalon | Tricentis | Test NG | Rest Assured Test Coverage is not Measurable 2 Hrs / e2e API test using any of the above tools. Gets harder with complexity Testing queues is not possible Need to write manual scripts for testing database With HyperTest Measurable. Reports exact lines of code covered 1 minute / end to end API test. 3 months of automation effort in 5 hours Only tool that tests queues (both Producers & Consumers) Fully autonomous database testing 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. 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 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

Confidently implement effective mocks for accurate tests. 07 Min. Read 8 April 2024 Simplify Your Code: A Guide to Mocking for Developers Shailendra Singh Vaishali Rastogi WhatsApp LinkedIn X (Twitter) Copy link You want to test your code but avoid dependencies? The answer is “ Mocking ”. Mocking comes handy whenever you want to test something that has a dependency. Let’s talk about mocking first in a little detail. What’s mocking, anyway? The internet is loaded with questions on mocking, asking for frameworks, workarounds and a lot more “ how-to-mock ” questions. But in reality, when discussing testing, many are unfamiliar with the purpose of mocking. Let me try by giving an example: 💡 Consider a scenario where you have a function that calculates taxes based on a person's salary, and details like salary and tax rates are fetched from a database. Testing with a database can make the tests flaky because of database unavailability, connection issues, or changes in contents affecting test outcomes. Therefore, a dev would just simply mock the database response i.e. the income and tax rates for the dummy data he is running his unit tests on. By mocking database interactions, results are deterministic which is what devs desire. Hope the concept is clear now, but when everything seems good with mocking, what’s the purpose of this article? Continue reading to get the answer to this question. All seems good with mocking, what’s the problem then? API mocking is typically used during development and testing as it allows you to build your app without worrying about 3rd party APIs or sandboxes breaking. But evidently, people still got some issues with mocking! Mocking Too Much is still a hot topic of discussion among tech-peers, but why do they have this opinion in the first place? This article is all about bringing out the real concerns people have with mocking. And presenting you a way that takes away all the mocking-related pain. 1️⃣ State Management Complexity Applications flows are fundamentally stateless. But database imputes state in a flow because it makes a flow contextual to a user journey. Imagine testing checkout, to do so the application should be in a state where a valid user has added a valid SKU with the required inventory. This means before running a test, we need to fill the database with the required data, execute the test, and then clean out the database once the test is over. This process, however, repetitive, time-consuming and with diminishing returns. Now, consider the complexity of handling numerous user scenarios. We'd have to prepare and load hundreds, maybe thousands, of different user data setups into the database for each test scenario. 2️⃣ False Positives/Negatives False positives in tests occur when a test incorrectly passes, suggesting code correctness despite existing flaws. This often results from mocks that don't accurately mimic real dependencies, leading to misplaced confidence. Conversely, false negatives happen when tests fail, indicating a problem where none exists, typically caused by overly strict or incorrect mock setups. Both undermine test reliability—false positives mask bugs, while false negatives waste time on non-issues. Addressing these involves accurate mock behavior, minimal mocking, and supplementing with integration tests to ensure tests reflect true system behavior and promote software stability. 3️⃣ Maintenance Overhead Assume UserRepository is updated to throw a UserNotFound exception instead of returning None when a user is not found. You have to update all tests using the mock to reflect this new behavior. # New behavior in UserRepository def find_by_id(user_id): # Throws UserNotFound if the user does not exist raise UserNotFound("User not found") # Updating the mock to reflect the new behavior mock_repository.find_by_id.side_effect = UserNotFound("User not found") Keeping mocks aligned with their real implementations requires continuous maintenance, especially as the system grows and evolves. HyperTest’s way of solving these problems We have this guide on why and how on HyperTest , just go through it once and then hop over here. To give you a brief: 💡 HyperTest makes integration testing easy for developers. What’s special is its ability to mock all the third-party dependencies including your databases, message queues, sockets and of course the dependent services. This behavior of autogenerating mocks that simulate dependencies not only streamline the test creation but also allow you to meet your development goals faster. The newer approach towards mocking Let’s understand this HyperTest approach by quoting an example scenario to make things easy to understand and explain. So imagine we’ve a shopping app and we need to write integration tests for testing it. 💡 The Scenario Imagine we have a ShoppingCartService class that relies on a ProductInventory service to check if products are available before adding them to the cart. The ProductInventory service has a state that changes over time; for example , a product might be available at one moment and out of stock the next. class ShoppingCartService: def __init__(self, inventory_service): self.inventory_service = inventory_service self.cart = {} def add_to_cart(self, product_id, quantity): if self.inventory_service.check_availability(product_id, quantity): if product_id in self.cart: self.cart[product_id] += quantity else: self.cart[product_id] = quantity return True return False 💡The Challenge To test ShoppingCartService 's add_to_cart method, we need to mock ProductInventory 's check_availability method. However, the availability of products can change, which means our mock must dynamically adjust its behavior based on the test scenario. 💡Implementing Stateful Behavior in Mocks To accurately test these scenarios, our mock needs to manage state. HyperTest’s ability to intelligently generate and refresh mocks gives it the capability to test the application exactly in the state it needs to be. To illustrate this, let's consider the shopping scenario again. Three possible scenarios can occur: The product is available, and adding it to the cart is successful. The product is not available, preventing it from being added to the cart. The product becomes unavailable after being available earlier, simulating a change in inventory state. HyperTest SDK will record all of these flows from the traffic, i.e., when the product is available, when the product is not available and also when there’s a change in the inventory state. In its test mode, when HyperTest runs all the three scenarios, it will have the recorded response from the database for all, testing them in the right state to report a regression if either of the behaviors regresses. I’ll now delve into how taking advantage of HyperTest’s capability of auto-generating mocks one can pace up the work and eliminate all the mocking-problems we discussed earlier . 1. Isolation of Services for Testing Isolating services for testing ensures that the functionality of each service can be verified independently of others. This is crucial in identifying the source of any issues without the noise of unrelated service interactions. HyperTest's Role: By mocking out third-party dependencies, HyperTest allows each service to be tested in isolation, even in complex environments where services are highly interdependent. This means tests can focus on the functionality of the service itself rather than dealing with the unpredictability of external dependencies. 2. Stability in Test Environments Stability in test environments is essential for consistent and reliable testing outcomes. Fluctuations in external services (like downtime or rate limiting) can lead to inconsistent test results. HyperTest's Role: Mocking external dependencies with HyperTest removes the variability associated with real third-party services, ensuring a stable and controlled test environment. This stability is particularly important for continuous integration and deployment pipelines, where tests need to run reliably at any time. 3. Speed and Efficiency in Testing Speed and efficiency are key in modern software development practices to enable rapid iterations and deployments. HyperTest's Role: 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 external factors. 4. Focused Testing and Simplification Focusing on the functionality being tested simplifies the testing process, making it easier to understand and manage. HyperTest's Role: Mocking out dependencies allows testers to focus on the specific behaviors and outputs of the service under test, without being distracted by the complexities of interacting with real external systems. This focused approach simplifies test case creation and analysis. Let’s conclude for now HyperTest's capability to mock all third-party dependencies provides a streamlined, stable, and efficient approach to testing highly inter-dependent services within a microservices architecture. This capability facilitates focused, isolated testing of each service, free from the unpredictability and inefficiencies of dealing with external dependencies, thus enhancing the overall quality and reliability of microservices applications. 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Get all your HyperTest-related questions answered and unlock the power of automated testing for flawless microservices. Get to Know HyperTest Category Contribution Error Handling General Integration Mock Verification Mocking Setup Test Execution Test Maintenance Test Results Testing Troubleshooting Usage Use Cases Clear all How can I contribute to the development of HyperTest? At the moment, HyperTest is a closed-source project. However, we welcome your feature requests! Simply submit them to our support team, and we'll do our best to accommodate them in future updates. Is there support for versioning of API mocks to align with API version changes? Currently, HyperTest treats APIs with different versions as unique entities, without grouping them for version control. This means there's no specific provision to manage versioning of API mocks to align with API version changes. How to use HyperTest for load testing by simulating high volume of outbound calls? HyperTest is not designed specifically for load testing, other popular solutions specialize in this area. You might consider exploring tools like Apache JMeter, Gatling, LoadRunner, K6, or Locust for conducting load testing effectively. These tools offer robust features specifically tailored for load testing scenarios. Can HyperTest handle mocks for authenticated API calls? Indeed! HyperTest manages authenticated API calls by capturing the headers containing the authentication token. To address token expiry, we've customized the behavior of the jsonwebtoken library by patching it on runtime so that it doesn't throw errors when encountering expired tokens. This ensures that your authenticated routes remain valid at all times. What should I do if I see ‘Mock not found’? There are two scenarios in which you could encounter this issue: 1. You’ve added a new outbound call to your existing API and we haven’t captured that mock. In this case, you should re-record a new API interaction that has this outbound mock and delete the older one. 2. HyperTest was not able to identify the mock properly from the available mocks for the test. In this case, you should reach out to the support team to raise this issue. How can I request features or report bugs for HyperTest? You can reach out to our support team via email [[ INSERT_EMAIL_ID ]]. What are the common issues to look out for when using HyperTest? App crashes after integrating HyperTest SDK: There could be several reasons for App crashes, lets discuss one by one: 1. Incorrect configuration: Please make sure you provide the right configure while intializing HyperTest SDK, e.g., serviceId & apiKey. 2. Using unsupported version of programming language or Run time environments: As of now HyperTest supports Node.js versions >= v14. 3. Your application depends on some 3rd party service to provide boot configuration: If you're calling an external service to fetch boot configurations then it could cause issues as we would try to mock all the external calls and not having relevant config at boot during a test run might cause unexpected issues. To solve this you should put this call inside an unmocked context so that the call goes through all the time, you can find more about it here. 4. Unknown error originating from your dependency: If you're getting this error only when running you app with HyperTest SDK in place then it is possible that we have instrumented this dependency and it has some bug which is causing this error, in such case please report this issue to our support team and we will try to resolve it as soon as possible. 5. Cannot start server because port is not available: HyperTest internally spins up two servers and they listen on available ports of these ranges: SDK server: 3001-3010 and CLI server: 4001-4010. You can verify if your application is using the same port as one of these server by passing this env variable to HyperTest CLI: "DEBUG=ht*", this will log the ports being used. If found same port is being used then please change port of your end to solve this. Seeing MOCK_NOT_FOUND during test runs: There are two scenarios in which you could encounter this issue: 1. You’ve added a new outbound call to your existing API and we haven’t captured that mock. In this case, you should re-record a new API interaction that has this outbound mock and delete the older one. 2. HyperTest was not able to identify the mock properly from the available mocks for the test. In this case, you should reach out to the support team to raise this issue. Requests not getting captured: There could be several reasons for this, lets discuss one by one: 1. Use of incorrect serviceId: Please make sure you've copied the correct serviceId from the dashboard and you're viewing requests of that service only. 2. Initializing SDK before everything else: HyperTest works by monkey patching your dependencies, it is mandated that you initialize hypertest before doing anything in your entry file so that it patches all the dependencies before being used, if we dont do this then you might see missing mocks or requests. 3. Issue with Opentelemetery Trace Exporter: To debug further you can try setting env variable "DEBUG=ht*" to see debug logs by HyperTest SDK. If you see any log that starts with: "HT-ERROR: Failed to export traces:" then it's an issue from our end. In this case, you should reach out to the support team to raise this issue. Seeing exceptions in CLI test results: If you're seeing that exception count is more than 0 then you should look at the logs for app crashes and you will see the id of the test case under question, you can view the particular test case on the dashboard and figure out why it is crashing the app. If it's a stale request that doesn't resonate with the current state of your application then you can discard it. If this exception is unexplainable and you don't find a test case id in the logs then you can try turning on the debug logs for the CLI by setting this env variable "DEBUG=ht*" to see the detailed logs. No mocks for expected outbound calls: Usually, this means that we have not provided support for this dependency or there's a bug in the HyperTest instrumentation, in any case, please reach out to the support team to raise this issue. Where can I find logs or error messages generated by HyperTest? You will see HyperTest SDK logs in your Applications “stderr” output for enhanced debugging you can turn on debug logs by setting this environment variable “DEBUG: ht*”. How do I update or remove outdated mocks? You can accept changes found in the test report to update the mock, if they are not being used then accepting changes will remove that mock from the test, but you cannot manually update it. If a test seems outdated, you can record a new one and delete the old one from the dashboard. What should I do if HyperTest is not capturing certain outbound calls? First, verify if the dependency responsible for the outbound call is supported by HyperTest (check here). If it is, there may be a bug, and you should contact our team for assistance. If it's not supported, please submit a request to our team for adding support for that specific dependency. We'll prioritize adding support as soon as possible. Does HyperTest support distributed tracing for complex microservices interactions? HyperTest doesn't offer distributed tracing, meaning it doesn't capture all traces it receives. However, it does keep a record of unique server requests (test cases) and tracks the flow of interactions between different microservices. While this specific feature isn't available yet, it's planned for future implementation. Is HyperTest compatible with microservices architectures? Absolutely! HyperTest seamlessly integrates with microservices architectures. Developers can easily visualize their service mesh and the interactions between services. Plus, they'll receive alerts about any new changes made to upstream or downstream service contracts. Although this feature isn't currently available, it's on the roadmap and will be implemented soon.

Discover the top 10 software testing tools for 2025 to streamline your testing processes and ensure high-quality software delivery. 19 April 2024 06 Min. Read Top 10 Software Testing Tools for 2025 WhatsApp LinkedIn X (Twitter) Copy link Download the Checklist What are Testing Tools? As we enter 2025, software development is witnessing an unprecedented growth and innovation. One of the key pillars supporting this evolution is the ecosystem of software testing along with software testing tools. Software testing is an aspect of the software development life cycle that ensures the quality, functionality, security and performance of applications. It is a process that is systematically designed to identify defects, errors or any discrepancies in the software to deliver a reliable and glitch-free software product. At the heart of this process are software testing tools which are indispensable instruments that facilitate and streamline the testing efforts. Understanding Software Testing: Software testing is a methodical examination of a software application to ensure that it behaves as expected and meets the specified standards and requirements. It involves the execution of a program or system with the intent of identifying any errors, gaps or areas of improvement. The ultimate goal is to provide stakeholders with confidence in the software's reliability and functionality. Understanding Software Testing Tools: Software testing tools are applications designed to automate, streamline and enhance the software testing process. These tools aid in efficiently executing test cases, managing test data and analyzing results which results in the reduction of manual effort and the increase of the effectiveness of the testing effort. The use of software testing tools has become necessary today due to rapid releases and continuous integration being the norm. These software testing tools come in various forms, serving different testing purposes. Automated testing tools such as HyperTest , Selenium, Appium, and JUnit, automate repetitive testing tasks, accelerating the testing process and ensuring consistent test execution. Performance testing tools like Apache JMeter and LoadRunner assess the application's scalability, responsiveness and stability under varying conditions. Security testing tools like OWASP ZAP and Burp Suite focus on identifying vulnerabilities, securing sensitive data and fortifying the software against potential cyber threats. Each category of software testing tools plays a role in ensuring the overall quality and reliability of the software. Software testing tools are indispensable assets in the software development lifecycle, contributing to the efficiency, accuracy and reliability of the testing process. Top 10 Software Testing Tools Software testing tools act as warriors against bugs and glitches. With a host of options available, choosing the right tool can be a hard task. Here are the top 10 software testing tools available today. 1. HyperTest - Software Testing Tool HyperTest is an API test automation platform that helps teams generate and run integration tests for their microservices without writing a code. HyperTest helps teams implement a true " shift-left " testing approach for their releases, which means you can catch all the failures as close to the development phase as possible. This has shown to save up to 25 hours per week per engineer on testing. With a focus to build autonomous integration tests, HyperTest has already helped teams like Nykaa, Groww, Urban Company etc in identifying the integration issues within their app and making sure they run smoothly after HyperTest's SDK implementation. 👉 Try HyperTest Now 2. Selenium - Software Testing Tool Selenium is a veteran in the testing arena. Offering flexibility and support for multiple programming languages, Selenium empowers both beginners and experts to craft tailored test scripts. It is like a customizable toolkit, allowing you to tackle web application testing with precision and control. While requiring some coding knowledge, Selenium's vast community and online resources make it a valuable asset for testing and development teams. Example: A startup leverages Selenium to write automated tests for their e-commerce website, ensuring smooth product browsing and checkout experiences across different browsers. 3. TestRail - Software Testing Tool TestRail transforms test case management into a smooth symphony. Its intuitive interface and features allow teams to collaboratively create, track and execute test cases with ease. It is a centralized hub where test plans are organized, results are documented and communication flows effortlessly. TestRail's integrations with software testing tools further enhance its value, keeping everyone on the same page throughout the testing process. Example: A large software company utilizes TestRail to manage test cases for their flagship product, ensuring clear communication and streamlined collaboration between testers, developers and stakeholders. 4. SoapUI - Software Testing Tool SoapUI specialises in testing the functionalities and performance of APIs. Imagine a dedicated tool for inspecting the internal workings of your software, ensuring communication between different components. SoapUI's intuitive interface and comprehensive testing features empower teams to validate APIs, identify and rectify issues and ensure integration with other parts of the system. Example: A financial services company uses SoapUI to test their payment processing APIs, guaranteeing secure and reliable transactions for their customers. 5. Testsigma - Software Testing Tool Testsigma champions accessibility and ease of use. Its cloud-based platform allows teams to collaborate and execute tests, regardless of location or device. Think of it as a testing platform accessible from anywhere, anytime and offering features like parallel testing and visual validation to expedite the process. Testsigma's focus on accessibility testing further ensures your software caters to diverse user needs. Example: A global education technology company uses Testsigma to test their learning platform across various devices and regions, ensuring accessibility for students with disabilities worldwide. 6. Appium - Software Testing Tool Appium's open-source nature and integration with software testing frameworks make it a cost-effective and versatile option for mobile testing endeavors. It is a software testing tools specifically designed to test mobile applications on real devices or emulators. Example: A social media application uses Appium to test their application on various Android and iOS devices, guaranteeing consistent functionality and a delightful user experience for their mobile audience. 7. Cucumber - Software Testing Tool Cucumber advocates for behavior-driven development (BDD) . Cucumber's readable syntax and focus on user behavior make it a valuable asset for testing teams seeking to bridge the communication gap and ensure the software aligns with user expectations. It translates user stories into executable test cases, fostering a shared understanding between developers and testers. Example: A gaming company uses Cucumber to test their new game features based on user stories, ensuring the gameplay aligns with player expectations and delivers an engaging experience. 8. Testmo - Software Testing Tool Testmo uses the power of AI (Artificial Intelligence) to enhance testing efforts. Testmo's AI capabilities also analyse test results and identify issues, providing valuable insights to improve your testing strategy. It suggests automated test cases based on the application’s behaviour, saving time and effort while ensuring complete coverage. Example: A retail company uses Testmo to automate regression testing of their website, using AI to suggest new test cases based on user traffic patterns and identify performance issues. 9. Tricentis Tosca - Software Testing Tool Tricentis Tosca caters to organisations seeking a complete automation suite. Tosca offers a wide range of features - from record-and-playback functionalities to data-driven testing, empowering teams to automate various testing types and achieve comprehensive coverage. It is a platform that combines test case management, automation tools and AI-powered insights into a single solution. Example: A large financial institution uses Tricentis Tosca to automate functional and performance testing of their core banking applications, ensuring consistent performance and reliability for their customers. 10. Katalon Studio - Software Testing Tool Katalon Studio caters to all software testing needs with a single platform. Katalon Studio's user-friendly interface and extensive features make it a valuable option for teams seeking a comprehensive testing solution without the need for multiple tools. It is a tool that offers web, mobile, API and desktop testing functionalities all under one roof. Example: A small software development company uses Katalon Studio to test their web and mobile applications, streamlining their testing process and ensuring efficient resource allocation. Know About - Katalon Alternatives and Competitors Conclusion The benefits of rigorous software testing are manifold. ➡️Firstly, it enables the early detection and elimination of bugs, reducing the cost and effort associated with fixing issues in later stages of development. ➡️Secondly, software testing contributes to improved code quality, efficient performance and enhanced user satisfaction. By identifying security vulnerabilities, testing also plays a big role in safeguarding sensitive data. The choice for the right software testing tools is entirely dependent on specific needs and preferences. Factors like team size, technical expertise, budget and the types of applications being tested need to be considered. Online resources should be used, and different options should be experimented with to find the perfect fit for testing. Investing in the right tools enables testing and development teams to deliver high-quality software, create user trust and stay ahead of the competitive software curve. From comprehensive platforms like HyperTest that cover various testing aspects to specialized tools like Selenium and SoapUI focusing on web and API testing , each tool brings unique features and capabilities to the testing ecosystem. The strategic adoption of these tools becomes essential for organizations aiming to deliver high-quality software in a timely manner. Related to Integration Testing Frequently Asked Questions 1. What is a software testing tool? A software testing tool is a program or application designed to assist software testers in performing various testing activities, such as test case creation, test execution, bug tracking, and test result analysis. 2. Why is Shift-Left Testing important? The best software testing tool depends on factors such as the specific testing needs, budget, technology stack, and team expertise. Popular software testing tools include HyperTest, Selenium, JIRA, TestRail, LoadRunner, and Postman, among others. 3. How to do software testing? To do software testing, follow these general steps: a. Identify the testing objectives and requirements. b. Plan and design test cases based on requirements. c. Execute test cases manually or using automated testing tools. d. Record and analyze test results, reporting any defects found. e. Retest fixed defects and ensure software meets quality standards before release. For your next read Dive deeper with these related posts! 11 Min. Read What is Software Testing? 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