Software testing automation tools -The surge in APIs means a respective demand for efficient API software testing to ensure they meet the required standards for functionality. 24 May 2023 10 Min. Read Efficient API Software Testing: Your Handy Guide WhatsApp LinkedIn X (Twitter) Copy link Access the 101 Guide The astounding market growth in API testing resonates with the boom in cloud applications and interconnected platforms that call for application programming interfaces (APIs). APIs work more like a contract where two parties agree about sending, receiving and responding to communication according to a set of predefined protocols. The surge in APIs means a respective demand for efficient testing to ensure that they meet the required standards for functionality, reliability, performance, and security. Without effective testing, the APIs could collapse or fail to perform impacting applications, services and business processes. Before we get into the nuances of API testing, let’s get a deeper understanding of what an API is, how it works and the context for API testing. What is (API) Application Programming Interface? API is a set of routine protocols and tools for creating software applications that are effectively synced together. It acts as a powerful intermediary between the application and the web server, seamlessly coordinating the ways the two systems interact by paying heed to the set of instructions. In other words, APIs are a simplified way to link your own infrastructure through cloud-centric app development, simultaneously permitting you to share your data with external users or clients. Public APIs are fundamental to businesses as they can simplify and build your connections and interactions with your partners. APIs give you flexibility while designing new products or tools. They open the door for innovation and simplify design. This makes administration and use easy, helping businesses and IT teams to collaborate efficiently. What causes API failures? At times, APIs do not work the way as expected due to technical or operational glitches like slow servers or connectivity, curbs by the API service vendor on subscriptions, area etc, security issues or DDoS attacks. API failures refer to the gaps that thus arise in the communication between two servers or teams. They can fail for multiple reasons. Some of the most common reasons for API failures are: ➢ Unexpected or unrecorded software changes, ➢ Communication hiccups between teams, ➢ Bad data that is incompatible with an API As software updates may not immediately register in the documentation, it can cause API glitches. An API call that worked in one version of the other program may not be compatible with the new version. An API call can be a link in a series, navigating data from upstream to downstream, and then passing the response on, either as a reply to the upstream data or sending it in a new direction. Since the origin of data is not always traceable, APIs could fail if the received data is not in the required format or in the format that the third party expects - for instance, in unacceptable characters. Also, backward compatibility may be accessible only for a limited grace period and after that non-updated API calls will not work. And if the API calls have been integrated in your code for a while, the sudden change in status may not be recorded. You will come to know only when they suddenly fail. API testing for enhanced business processes Effective API testing helps in: Checking the functioning of the software An API Testing sees that the software systems work uniformly during the unit testing phase of the development cycle. It is done to check the reliability, performance and functioning of the software. Resolving the errors In addition to this, the API testing organises the API endpoints. It helps the software programmer choose between the automation tool and the verification methods. The procedure detects the bugs at an early stage. API tests involve the entire software system and verify that all the components function as expected while other categories of testing, like unit tests verify the functionality of individual components within a single application. The broader test span of API makes it easier to identify any bugs in the unit, database, and server levels. API tests are also faster to run and more isolated than UI tests. According to data from Andersen Lab , a UI test runs for approximately seven minutes while an API test runs for 12 seconds. API Testing is important to assess that the API functions properly and can process the requests that are made. It should analyze the responses that include data quality, confirmation of authorization and reply time. API Testing is done consistently at appropriate times to make the systems run meticulously. ● Is highly effective It requires the use of fewer codes and can provide a better test coverage. Most systems have APIs and services with some specifications with the help of which one can create automated tests easily. ● Has a remarkable performance A common UI regression test suite can take 8-10 hours to operate. But an API testing system takes 1-2 hours. It is more reliable than the ordinary testing procedures and does not take hours to work. ● Does not have any language issues Any language can be used to develop the application. As the data is exchanged using XML and JSON, the language does not matter. ● Integrates with the GUI testing One can test the API without an interface. However, the GUI tests can be conducted after the API testing is done. It would allow new users to get familiarised with the programme before the test. Essentially, the API integration testing is the evaluation of the API interfaces to see if these are functioning optimally. Some of the most-popular API integration testing tools are Postman, jmeter, assertible and rest-assured. ● Reduces the testing cost The API testing can detect bugs, technical issues and teething problems at an early stage. This helps save time and money in the long run. As the errors are rectified during the initial stages, there is no scope of excessive spending. Types of API Testing API Testing must be done at the earliest stages. These ensure that the software works impeccably well and allows access to the stored data. Different tests evaluate the aspects of the API procedure and are necessary to guarantee a hassle-free digital interaction. 1. Load Testing The API load testing is done to ensure that the software applications can take on the load that the user wants them to. The API load testing tools place load on the real apps, software and websites in a controlled environment. 2. Performance testing Similarly, the API performance testing tools evaluate the ways in which API performs under a set of conditions. It is important as it identifies any issues in the API during the early stages. For instance, the nodejs API Testing is a toolkit that acts as an intermediary between C/C++ code and Node Java Script Engine. For example, the jmeter performance testing is used for web applications. With a user-friendly interface, it works on a multi-threaded framework. 3. Security Testing In this, the programmers see that the API is secure from all the external threats that might jeopardize its efficiency. If the data falls into wrong hands and is misused, the program might go haywire. The security testing sees whether the basic security requirements have been fulfilled that include access to the users, authentication concerns and the encryption. 4. Unit Testing This checks the functioning of the individual operations. It includes testing the codes, checking if the units perform well individually and is sometimes referred to as the White Box Testing . Also, it is the first step in assessing the API and helps determine the quality control process. The individual parts are tested so that these work uniformly when put together. 5. Functional Testing It includes testing different functions in the code-base. API functional testing is done with some procedures that require attention to detail. The software developers can check the data accuracy and the response time along with the authorization issues. The error codes and the HTTP status codes must be tested accurately. Practices/Methods of API Testing ● Segregate API Test cases into test categories. ● Prioritise API function calls to facilitate fast testing. ● Include the declarations of the APIs called at the top of each test. ● Provide accurate parameters in the test case. ● Keep the test cases self-contained and independent. ● Avoid test chaining in your development ● Send a series of API load tests to check the expected results and assess the efficiency of the system. ● Give attention while dealing with single call functions such as CloseWindow, Delete etc. ● Plan and perform call sequencing meticulously ● Ensure impeccable test coverage by creating API test cases for all possible API input combinations. Challenges in API Testing ● The most challenging aspects of Web API testing are parameter combination, parameter selection, and call sequencing. ● There is no graphical user interface to test the application, making it impossible to provide input values. ● For testers, validating and verifying output in a different system is a little complicated. ● The testers must be familiar with parameter selection and classification. ● You must test the exception handling function. Coding knowledge is a must for testers. Types of Bugs that API testing detects ● Functionalities that are duplicated or missing ● Unused flags. ● Security concerns. ● Issues related to multi-threading. ● False alerts errors/warnings to a caller. ● Improper dealing of valid argument values. ● Performance issues ● Dependability issues like difficulty in connection and receiving responses from the API. HyperTest & API TESTING HyperTest is a tool that eliminates the bugs and errors by integrating the applications and providing an exemplary software development. It ensures an outstanding quality and covers all forms of testing such as regression, API and integration. It can be set up in less than five minutes and provide results within a jiffy. The tool is extremely reliable and does away with the traditional methods of manual testing. It does not require an external set-up and seamlessly integrates with all the applications and interfaces. It detects and resolves all the errors before release and can increase the testing coverage. Why HyperTest Tool for API Testing? The HyperTest is suitable for the API testing procedures as it nips all the evils in the bud and provides a worthwhile digital experience. Businesses rely on the tool to assist them in the process of developing testing scripts and codes for a seamless online transaction. ● Provides complete coverage The HyperTest provides more than 95% of the app in less than 5 minutes. It is superior to other tools as it does away with the manual effort of writing scripts. Also, it helps the Devops pass on cleaner builds to the QA guys. This lessens the time taken to test an application. It auto-generates the tests by providing reliable results. It does not require manual testing that makes the teams work endlessly and develop the test scripts. Moreover, it is an API management tool that ensures security and performance. It solves the problems of API regression and makes the team focus on developing the software. It resolves the errors at the source by checking for the API issues during the nascent stages. ● Builds dynamic assertion The auto-generated tests run on the stable version of the application to effectively generate assertions. This does not allow the business owners to reveal sensitive information about their company or let the data fall be misused. It reports any anomalies that could occur and the breaking changes that might be resolved at a later stage. It makes use of real-world scenarios to build tests. ● Is Unique and highly effective Numerous companies prefer the HyperTest API testing tool because it has a unique approach. It monitors the actual traffic on the application and makes use of real-world scenarios to build the tests. Also, the teams can get access to the complete coverage reports that highlight the flow of things in the automation process. ● Can quickly detect and resolve all the errors The tool provides solutions for the applications. It removes all the bugs, helps the businesses develop worthwhile strategies and safeguard the sensitive information. Some of the software engineers fail to detect the source of the errors and how to mitigate them. Traditional tools miss more errors than these detect. The HyperTest tool detected 91% more bugs and technical issues in the systems. ● Integrates with the services The tool follows an asynchronous mirroring process with no change in the application code or configuration. It has no impact on the function and the performance. As it is cloud-operated, all the data is present in the client’s environment and never gets leaked. It is never misused and hardly lands up in the wrong hands. ● Can efficiently manage the API testing procedures The HyperTest monitors the API 24/7 and reports all the failures. It is one of the best API testing tools that solves the problem of API regression . Moreover, it eliminates the redundant test cases by maximising the coverage. By creating real-time dynamic assertions, it reports the breaking changes. It saves the time of the developers and provides the Devops team ways to speed up their processes. It reports all the errors in an effective way and helps the Devops introduce some significant changes. According to a recent survey, HyperTest saves about 40% of the man hours that developers invest in figuring out the algorithms. ● Provides useful information The HyperTest provides all the data about the API artefacts and documents the details creating a reliable repository of information. Through the regression feature, it delivers accurate results. It brings to light all the API failures and monitors the entire application process. By mirroring the TCP requests, it does not impact the application code or the function. The cloud-based environment does not let any data escape from within. It examines all the minor code changes and reports the data accurately to the system. Apart from this the HYPERTest monitors the micro-services and provides sure-shot analysis. ● Manages the authentication process The HyperTest can manage the multi-factor authentication processes really well. It can easily write the customized requests and look into the data constraints. Summing it up, the API checks the malfunctioning or the errors that might surface during the exchange of information between the computer systems. The API testing ensures that the systems run smoothly and have no technical issues. The HyperTest tool develops efficient API testing procedures and manages the authentication process. It builds a dynamic assertion and effortlessly integrated with all the services. By providing complete test coverage and closely examining the software, it has become the most-sought after API testing tool by the businesses. Takeaway You may not be able to prevent APIs from failing, but you can contain the damage, and prevent an API failure from bringing down your application as well. With the HyperTest tool, you needn’t vex over API failures anymore. Ensuring round-the-clock monitoring, the platform provides effective solutions to the API regression. With the use of upgraded testing procedures, your data can be secure and free of any anomalies that might jeopardise your reputation. To browse through the features that make the testing platform stand out in functionality and reliability and acquaint yourself with the wide array of testing procedures visit our website . Frequently Asked Questions 1. What is API Software testing? API software testing involves evaluating the functionality, reliability, and security of application programming interfaces (APIs). It verifies that APIs perform as expected, handle data correctly, and interact seamlessly with other software components, ensuring their reliability and functionality. 2. Why is API testing important? API testing is vital because it ensures that software components communicate correctly. It validates functionality, data accuracy, and security, preventing errors and vulnerabilities, ultimately ensuring reliable and efficient interactions between different parts of a software system. 3. How to approach API testing? Approaching API testing involves several key steps. Begin by thoroughly understanding the API documentation to grasp its endpoints, inputs, and expected outputs. Next, identify various test scenarios, considering different data inputs and edge cases. Utilize dedicated API testing tools or libraries to create and execute test cases, sending requests and analyzing responses. Verify that the API functions as intended and handles errors gracefully. For efficiency, automate repetitive tests and establish a robust monitoring and maintenance system to adapt to ongoing API changes, ensuring continuous reliability and performance. For your next read Dive deeper with these related posts! 07 Min. Read What is API Testing? Types and Best Practices Learn More 08 Min. Read What is API Test Automation?: Tools and Best Practices Learn More 07 Min. Read Best API Testing 101: Practices You Should Follow Learn More

Nykaa wanted to improve how well their app is tested by adding more test case scenarios that closely simulate real-world usage. This way, they can quickly find and fix issues, aiming for a improved customer experience. Customer Success Processing 1.5 Million Orders, Zero Downtime: How Nykaa Optimizes with HyperTest Nykaa wanted to improve how well their app is tested by adding more test case scenarios that closely simulate real-world usage. This way, they can quickly find and fix issues, aiming for a improved customer experience. Pain Points: Inefficient automation introduced defects into the production environment. Extended release cycles constrained timely deployments. Insufficient code coverage resulted in undetected vulnerabilities. Results: Achieved 90% reduction in regression testing time. Improved release velocity by 2x. 90% lesser integration defects or incidents in production. About: Founded: 2012 Employees: 4168+ Industry: Beauty and Fashion E-commerce Users: 17 million+ Nykaa is India's premier lifestyle and fashion retail destination, providing a comprehensive array of products across cosmetics, skincare, haircare, fragrances, personal care, and wellness categories for both women and men. Nykaa made an impressive stock market debut, reaching a valuation of over $13 billion. The company's shares initially listed at an 82% premium and have climbed to approximately 96%. Listed on the BSE since November 2021, Nykaa now boasts a market capitalization of $8.3 billion, underlining its significant impact and strategic presence in the beauty and lifestyle market. Nykaa's Requirements: High fidelity integration testing for a service oriented architecture. Refined automation processes to deliver tangible outcomes. Improved code coverage to minimize production defects. Challenge: Operating a dynamic e-commerce platform with daily orders exceeding 70,000, Nykaa recognized the need for a sophisticated testing approach suitable for their rapidly growing microservices. They had implemented an automation suite to safeguard their revenue and prevent defects from reaching production. Despite the deployment of a new automated system, occasional defects still appeared production. Initial automation efforts were inadequate, not fully preventing defects and causing the team to shift focus toward managing disruptive changes linked to microservice expansion. Integration testing was excessively time-consuming, with many defects originating from backend systems, affecting release velocity and product quality. Low code coverage in earlier stages meant that many potential issues went undetected until later in the development cycle, increasing risk and remediation costs. Solution: Nykaa adopted HyperTest to enhance automation and effectively test their services expansion , aiming to prevent potential disruptions. This solution streamlined their feature release process, allowing for comprehensive testing without separate test setups. HyperTest facilitated rapid integration testing for microservices, reducing the testing time from several days to mere minutes—a 70% increase in testing efficiency. This transformation boosted speed of feature releases by substantially shortened testing times. Additionally, with HyperTest, Nykaa achieved up to 90% code coverage, drastically reducing the incidence of critical bugs and vulnerabilities reaching the production environment. I have been using Hypertest for the past 2.5 years. It has made the QA cycle reliable providing the best quality, reducing a lot of manual effort, and thus saving functional bandwidth. The bugs which can be missed in automation can be easily caught with Hypertest. -Atul Arora, SDET Lead, Nykaa Read it now How Yellow.ai Employs HyperTest to Achieve 95% API Coverage and Ensure a Flawless Production Environment Read it now Airmeet and HyperTest: A Partnership to Erase 70% Outdated Mocks and Enhance Testing Speed By 80% View all Customers Catch regressions in code, databases calls, queues and external APIs or services Take a Live Tour Book a Demo

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

Discover which code coverage metrics matter for engineering managers and which to ignore for effective software quality assessment. 19 February 2025 07 Min. Read Code Coverage Metrics: What EMs Should Measure (and Ignore) WhatsApp LinkedIn X (Twitter) Copy link Get Code Coverage with Real Context Engineering leaders often hear this claim: "We have 85% code coverage!" But here's an uncomfortable fact: An app with 95% coverage might still crash every hour An app with 70% coverage could be incredibly stable The key difference? The things we measure—and how we measure them. This guide will show you: The 5 coverage metrics that help predict how reliable a system is The 3 vanity metrics that teams waste their time trying to improve How to boost meaningful coverage without forcing 100% What Counts in Code Coverage? 1. Integration Coverage (Beyond just unit tests) Why Does This Matter? 58% of issues in production come from interactions between services that haven't been tested Unit tests on their own miss failures in APIs, databases, and asynchronous flows What should you track? How well your tests cover the ways different services, APIs, and third-party systems work together. Integration Coverage = (Tested Service Interactions / Total Interactions) × 100 An Example of Failure: A travel booking app boasted 90%-unit test coverage but failed to check how its flight API worked with Redis caching. When traffic peaked, the cached flight prices didn't match the database values leading to lost revenue. 2. Critical Path Coverage Making sure tests check the most important parts of how the code runs: ✅ where your code handles key business logic, has a big impact on other parts, and might break. Unlike basic line or branch coverage, which just sees if code ran critical path coverage looks at whether the right code was tested in real-world situations. Why It's Important? 20% of code deals with 80% of what users do Test login, payment, and main tasks first How a payment system handles errors is way more important than a small function that formats dates and times. 3. Mutation Coverage Why It's Important? Checks if tests find fake bugs (not just run lines) Shows "useless tests" that pass but don't check anything Tool Example: # Install mutation testing tool pip install mutatest # Check test effectiveness mutatest --src ./src --tests ./tests 4. Edge Case and Failure Scenario Coverage Many test cases don't dig deep enough. They check the logic with the given test data, and that too for scenarios we already know about. This can lead to hidden bugs that cause problems when the system is up and running. Why This Matters? Tests that follow the expected path are simple; systems tend to break in unusual situations. Things to keep an eye on Tests for situations like network delays wrong inputs, and usage limits. Generating tests from real traffic, capturing rare edge cases and failure scenarios as they happen in live environments can ensure comprehensive coverage, identifying hidden bugs before they impact users. Learn more about this approach here. 5. Test Quality (not just quantity) Code coverage doesn't guarantee test quality on its own—it shows which lines ran, not why they ran or if critical paths underwent testing. Without context, teams create shallow tests that boost coverage but overlook real risks. What to track: Assertion Density : Do tests validate outcomes or just run code? Flakiness Rate : % of tests that fail. Bug Escape Rate : Bugs found in production compared to those caught by tests. What to Ignore? (Despite the Hype) 1. Line Coverage % Alone It tells you which lines of code ran during tests but not if they underwent meaningful testing. A high percentage doesn't ensure that edge cases, failure scenarios, or critical logic have been checked. For instance, an if condition might run, but if the happy path executes potential failures stay untested. The Trap: Teams cheat by creating basic tests Fails to capture why the code ran Coverage % Production Incidents 92% 18/month 76% 5/month The Fix: Give top priority to “branch + integration coverage” and show gaps in complex logic. ✅ HyperTest solves this problem. It creates tests from actual traffic. This makes sure real-world scenarios cover execution paths, not just hitting code lines. 2. 100% Coverage Mandates While full branch or line coverage ensures that everything in the code is executed, it does not ensure that the tests are useful. Coverage targets lead teams to write shallow tests to satisfy the metric, without verifying actual behavior, edge conditions, or error handling. Why It Backfires: Engineers waste time debugging boilerplate code (getters/setters) Produces false confidence in vulnerable systems "Shoot for 90% critical path coverage, not 100%-line coverage.". – OpenSSF Best Practices ✅ HyperTest addresses this by automatically generating tests from actual traffic, so 100% coverage is not a phrase but actual execution patterns, dependencies, and real-world scenarios. 3. Coverage without Context They all aim for strong code coverage but without context, it is worth nothing. Code is executed within tests without regard to its application or interactions, so there are gaps. Scenario: Contextless Coverage in an Online Shopping Checkout System Assume that an e-commerce site has a checkout process with: Utilizing promo codes Location-based calculation of tax Payment processing via multiple gateways There is a team of individuals who write tests that execute all these operations, with 90%+ line coverage . But these tests only follow a happy path —valid coupon, default tax zone, and successful payment. Why Does Coverage Without Context Fail? Experiments do not verify expired or invalid coupons . They do not verify edge cases , i.e., exemptions from tax or cross-border purchases. Payment failures (lack of funds, API timeouts) are not tested. Even with excellent line coverage, critical failures can still occur at production time because the tests lack real-world execution context . ✅The Solution: HyperTest achieves this by constructing tests out of real traffic, capturing real execution flows and dependencies. This renders coverage predictive of real behavior, rather than code execution. How to Improve Meaningful Coverage (without the grind)? ✅ Automate Test Generation HyperTest helps teams achieve 90%+ code coverage without writing a single test case by auto-generating tests based on real API interactions. ➡️ How It Works? Captures Real Traffic : It observes real API requests and responses during actual usage. Auto-Generates Tests : HyperTest converts these interactions into test cases, ensuring realistic coverage. Mocks External Services : It auto-generates mocks for databases and third-party APIs, eliminating flaky dependencies. Runs Tests Automatically : These generated tests run in CI/CD, continuously validating behavior. Identifies Gaps in Coverage : HyperTest highlights untested code paths, helping teams improve coverage further. See how automated testing works in 2 minutes. Try it yourself here. ✅ Prioritize by Impact Framework: Tag endpoints by business criticality Allocate test effort accordingly Criticality Test Depth P0 (Login) Full mutation tests P2 (Admin) Happy path + edge The Bottom Line Code coverage isn’t about hitting a number, it’s about trusting your tests . And if used correctly, it can: ✅ Prevent production outages ✅ Accelerate feature delivery ✅ Reduce debugging time By focusing on integration paths, critical workflows, and mutation effectiveness, teams can achieve: 63% fewer production incidents 41% faster CI/CD pipelines Ready to see real coverage in action? See How HyperTest Automates Coverage👇 Related to Integration Testing Frequently Asked Questions 1. What code coverage metrics should engineering managers focus on? Engineering managers should prioritize branch, statement, and mutation coverage for meaningful insights. 2. What is test scenario in manual testing? High coverage doesn’t guarantee quality—untested edge cases and poor test logic can still exist. Which code coverage metrics can be ignored? Line coverage alone is misleading; it doesn’t ensure logic paths are fully tested. For your next read Dive deeper with these related posts! 12 Min. Read Different Types Of Bugs In Software Testing Learn More 07 Min. Read Top 10 Code Coverage Tools Every Developer Should Know Learn More Add a Title What is Integration Testing? A complete guide Learn More

Discover the top unit testing tools of 2024 for robust code. Stay ahead in software development with our insightful guide. 9 January 2024 09 Min. Read Most Popular Unit Testing Tools in 2025 WhatsApp LinkedIn X (Twitter) Copy link Get a Demo By unit testing, developers can confidently change code without worrying about breaking other parts of the application. It encourages good design and simplifies maintenance. When it comes to unit testing, it is mainly the developer’s responsibility to ensure that the code they’re writing works perfectly at the component level. List of Top Unit Testing Tools ➡️ HyperTest ➡️ JUnit ➡️ NUnit ➡️ MSTest ➡️ Mocha ➡️ PHPUnit ➡️ RSpec ➡️ PyTest This article intends to focus on breaking the term “ unit testing ”, highlighting the importance of performing them before handing over the code to a tester for higher-level of testing. The market is flooded with unit testing tools, but not all the available tools serve the purpose of testing code in isolation before testing in integration. We’ll discuss the best tools that perfectly solves the purpose of testing code blocks before it starts to work as one complete entity. What Is Unit Testing? Unit testing focuses on verifying the functionality of individual components or "units" of code. Typically, a unit refers to the smallest testable part of an application, such as a function or a method within a class. The primary goal of unit testing is to ensure that each unit performs as expected in isolation from other parts of the application. 💡 Team IBM found that unit testing can reduce bug detection costs by up to 90%. It's cost-effective, not just code effective. Read more - What is Unit testing? A Complete Step by Step Guide ➡️ Isolation is Key The power of unit testing lies in its focus on isolation. By testing each unit independently, you eliminate dependencies on other parts of the code, providing clarity and precision in finding issues. ➡️ The Building Blocks Think of your software as a complex machine, where each function is a gear. Unit testing involves scrutinizing each of these gears separately, ensuring they perform exactly as intended under various conditions. HyperTest has made a unique way to test integrations between services with no need to manually create and update mocks, that is usually required in unit testing. More about the approach Why to Perform Unit Testing? Unit testing is about ensuring each individual unit of your code - typically functions or methods - works correctly in isolation, much like checking every brick in a wall. Unit testing your code not only gives confidence to devs to move it in the further stages of testing, but also lets them know whether they’re moving in the right direction or not. 👉Unit tests target specific functionalities, making it easier to pinpoint where and why a failure occurs. 👉Every time a change is made, unit tests can be rerun to ensure that existing functionalities are not broken. 👉With a comprehensive suite of unit tests, developers can refactor code - even make significant architectural changes - with confidence. 👉Unit tests catch issues at the earliest stage of development, significantly reducing the cost and time associated with fixing bugs later in the development cycle. Allowing bugs to slip into production is the ultimate nightmare for any growing company, resulting in significant harm, both financially and in terms of user trust. HyperTest has built a unique approach that catches all the regressions before it reaches production, as it is monitoring real production traffic 24*7. 💡 Learn more about HyperTest’s way of catching bugs before it moves to production here. How Unit Testing Works? The primary goal of unit testing is to validate that each unit of the software performs as designed. A unit is the smallest testable part of any software and typically has one or a few inputs and usually a single output. Let’s understand how unit testing works: 1. Choose a Framework: First, select a unit testing framework compatible with your programming language. This framework provides the tools and environment for writing and running tests. 2. Write Test Cases: For each unit of code, write test cases that cover various scenarios, including edge cases. These test cases should be simple, focusing on one aspect of the function's behavior. 💡 HYPERTEST monitors the network traffic and auto-generates test cases for all your services without needing them to be up and running Get a demo here . 3. Run and Review: Execute the tests and analyze the results. Any failure indicates a problem in the corresponding unit, guiding developers to the issue's source. 💡 HyperTest autonomously identifies relationships between different services and catches integration issues before they hit production, letting developer of a service know in advance when the contract between his and other services has changed, enabling quick remediation and collaboration. Learn the complete approach here . Choosing The Right Unit Testing Tool A good unit testing tool should aim to lessen your burden and give more power to devs, so that they get instant feedback on their code. Here are some key factors to consider when selecting a unit testing tool: Language and Framework Compatibility Ease of Integration Test Writing Ease Mocking and Isolation Features Reporting and Analysis Community and Ecosystem Performance and Scalability 8 Most Popular Unit Testing Tools We have covered both the free tools and the paid tools in the Unit testing category. The top 8 Most Popular Unit Testing tools to consider for 2024 are: ➡️ HyperTest ➡️ JUnit ➡️ NUnit ➡️ MSTest ➡️ Mocha ➡️ PHPUnit ➡️ RSpec ➡️ PyTest Let’s discuss these widely used unit testing tools in great details to have a better comparison. 1. HyperTest - Unit Testing Tool HyperTest aims to ease out unit testing by taking away the pain of manual mocking, and test data preparation. It is a modern no-code tool that is trusted by teams like Nykaa, PayU, Skaud, Zoop, Fyers etc. It actually saves my time in maintaining each and every test case and keeping a record of them. Also running each API in postman and seeing their responses one by one looks a hectic work now after being a HyperTest user. - Pratik Kumar, Fleek Technologies Pros of HyperTest: 1. Set up is just like how you will set up an APM, i.e., it's super easy. 2. Test can be run locally without needing dedicated test environments 3. Support all protocols like http, graphQL, gRPC, Kafka and AMQP to cater to more use cases. Adding more as we speak 4. Active de-duplication to reduce the number of requests run on REPLAY. We optimise for code coverage & filter requests that don't cover additional lines of code 5. Distributed tracing to help developers debug root cause faster. 6. Auto-updates mocks as dependencies change to keep test results trustworthy. How it works? Developers' set-up our SDK (2-lines) across any of their (backend) services and configure it to record traffic from any environment. When HyperTest works in RECORD mode it collects end to end trace of every incoming request i.e. the request, response and outbound calls. These requests (tests) can be replayed on a new build later to check for regressions in API responses and outbound calls. In the REPLAY mode HyperTest uses mocked responses of all dependent systems to keep tests non-flakey and results deterministic and consistent. 💡 Kick off with HyperTest for free and witness your code coverage soar above 90%. 2.. JUnit (Java) - Unit Testing Tool JUnit is a simple framework to write repeatable tests in Java. It is an instance of the xUnit architecture for unit testing frameworks. Uses annotations such as @Test for identifying test methods. Supports test fixtures with @Before and @After annotations for setting up and tearing down common test data. Pros of Junit: Wide adoption and community support. Integrates seamlessly with IDEs like Eclipse and IntelliJ IDEA. Annotations make the test code clean and easy to understand. Cons of Junit: Limited to Java, not suitable for other programming languages. Lacks advanced mocking capabilities natively. Use Case: Ideal for Java developers looking for a reliable and widely supported testing framework, especially in enterprise environments. 3. NUnit (C#) - Unit Testing Tool NUnit is an open-source unit testing framework for .NET languages. It's similar to JUnit but designed for the .NET environment. Supports data-driven tests and can run tests in parallel. Uses attributes like [Test] and [TestFixture] for identifying tests and test classes. Pros of Nunit: Strong parameterized testing capabilities. Suitable for parallel test execution. Has a strong assertion library. Cons of Nunit: Lesser integration options with .NET Core as compared to MSTest. Can be complex for beginners. Use Case: Best suited for .NET developers who need a robust and feature-rich framework, especially for complex applications. 4. MSTest (C#) - Unit Testing Tool MSTest is Microsoft's official testing framework, integrated into the Visual Studio IDE. It has built-in test runners and supports test categories for organizing tests. Uses attributes like [TestMethod] and [TestClass] . Pros of MSTEST (C#): Tight integration with Visual Studio and other Microsoft tools. Easy to use for developers familiar with the Microsoft ecosystem. Supports data-driven testing. Cons of MSTEST (C#): Not as feature rich as NUnit. Limited to the Microsoft ecosystem. Use Case: Perfect for teams heavily invested in the Microsoft ecosystem, particularly those using Visual Studio. 5. Mocha (JavaScript) - Unit Testing Tool Mocha is a flexible JavaScript test framework running on Node.js and in the browser, making asynchronous testing simple. Supports behavior-driven development (BDD), test-driven development (TDD), and other testing styles. Pros of Mocha (JavaScript): Wide range of plugins and integrations. Rich reporting and mapping exceptions to test cases. Easy asynchronous testing support. Cons of Mocha (JavaScript): Requires assertion libraries (like Chai) for assertions, as it does not come bundled with one. Can be slow with large test suites. Use Case: Suitable for JavaScript developers needing a versatile tool for both frontend and backend testing. 6. PHPUnit (PHP) - Unit Testing Tool PHPUnit is a programmer-oriented testing framework for PHP. It is inspired by JUnit and other testing frameworks. It follows the xUnit architecture. PHPUnit uses annotations to identify test methods and supports setup and teardown methods. Pros of PHPUnit (PHP): Widely used and well-documented. Supports data provider methods for data-driven tests. Good integration with many PHP projects and frameworks. Cons of PHPUnit (PHP): Can be challenging to set up with some PHP applications. Limited mocking capabilities compared to standalone mocking frameworks. Use Case: Essential for PHP developers, especially those working with frameworks like Laravel and Symfony. 7. RSpec (Ruby) - Unit Testing Tool RSpec is a behavior-driven development (BDD) framework for Ruby, allowing developers to write human-readable specifications for their code. It emphasizes the behavior of applications, using descriptions and expectations to define tests. Pros of RSpec (Ruby): Promotes readable and maintainable code. Highly customizable with support for hooks and fixtures. Strong integration with Ruby on Rails. Cons of RSpec (Ruby): Learning curve for BDD concepts. Can lead to verbose test suites. Use Case: Great for Ruby developers and teams practicing behavior-driven development to ensure code meets its behavior specifications. 8. PyTest (Python) - Unit Testing Tool PyTest is a no-boilerplate alternative to Python’s standard unittest module. Does not require classes for tests, supports parameterized testing, and can run unittest (including trial) and nose test suites out of the box. Pros of PyTest (Python): Simple syntax, easy to write and read tests. Powerful fixtures system for managing test state. Extensive plugin system. Cons of PyTest (Python): The learning curve for its advanced features. Sometimes slower than unit test for large test suites. Use Case: Great for Python developers at all levels, offering powerful yet user-friendly features for a wide range of testing needs. Conclusion Unit testing might seem like an extra step, but it's an investment. An investment in the quality of your product, the efficiency of your development process, and the satisfaction of your users. In the world of software development, it's not just a best practice; it's a vital one. So, as a developer, start embracing unit testing. It's not just about finding bugs ; it's about building a culture of quality and excellence in your code. Get started with HyperTest: https://www.hypertest.co/sign-up Happy testing! Related to Integration Testing Frequently Asked Questions 1. Which is best for unit testing? The choice for unit testing depends on the programming language and project requirements. Popular frameworks include JUnit for Java, pytest for Python, and Jasmine for JavaScript. Select the one that aligns with your language and provides clear, concise testing capabilities for your specific needs. 2. Why are unit testing tools essential? Unit testing tools are essential for software development because they ensure code reliability by detecting errors early in the development process. They help validate individual units of code, improve overall software quality, and facilitate easier maintenance and debugging. 3. What Type of Testing is Unit Testing? Unit testing is a type of testing that focuses on validating the smallest parts of a software application, typically individual functions or methods. It ensures each unit performs as intended in isolation, identifying and fixing defects early in the development cycle. 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 09 Min. Read Automated Unit Testing: Advantages & Best Practices Learn More

A Quick Comparison Between Top Backend Automation Testing Tools Download now Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

Get Your Test Automation Suite Up and Running in a Day, Ditch the Manual Efforts Required. 07 Min. Read 14 August 2024 5 Steps To Build Your API Test Automation Vaishali Rastogi WhatsApp LinkedIn X (Twitter) Copy link Writing and maintaining test cases with Postman was all fun, until there was no agile development. Taking all the time to create collections, fire API calls, test APIs and then maintain all that was a thing of past. Now that the time demands the engineering teams to build fast and release faster, Postman and such tools can’t be of much help. HyperTest, our autonomous integration testing tool, can take away all the manual efforts required in Postman. Developers of companies like Skaud, Yellow.ai , Porter, Purplle, Zoop etc are already ahead of their deadlines and are able to focus on making the application better instead of being trapped in the never-ending cycle of writing and maintaining test cases. HyperTest has significantly reduced my test maintenance workload. No more juggling countless test cases or manually tracking API responses on Postman. It's a game-changer! Pratik Kumar, FLEEK TECHNOLOGIES Here’s an easy 5 step guide to build a robust API test automation suite: 1️⃣ Pick any service and install HyperTest SDK 2️⃣ Deploy your service normally either locally or any other environment. HyperTest will record all the incoming and outgoing traffic of that service in that environment. 3️⃣ Go to HyperTest dashboard of all incoming and outgoing calls of this service put together as end-to-end inetgration tests 4️⃣ Install HyperTest CLI. Run these tests on a new build of your service. It will catch regressions across your service response and outgoing calls. 5️⃣ You can make HyperTest tests part of your CI pipeline using pre-push commit hooks and sign-off every release using these autonomous test suites. 1. Installing HyperTest SDK To begin, you'll need to install the HyperTest SDK and its CLI tool. These are the core components that enable HyperTest to interact with your application and manage API test automation effectively. The installation process is straightforward and can be done using package managers like npm for Node.js applications. Once installed , you need to initialize the HyperTest SDK in your application, which typically involves adding a simple configuration file or command to integrate HyperTest with your app's codebase. 💡 Get started with HyperTest within 10 minutes of installation and start catching regressions from the very start. 2. Start your Application in Record Mode After setting up the SDK, you'll need to start your application in "record mode." This mode enables HyperTest to monitor and capture all the outbound API calls your application makes. When your application runs in this mode, HyperTest listens to the requests and the corresponding responses, creating a record of interactions with external services. This recording forms the basis for generating mock data that will be used during regression testing. 3. Introduce Live Traffic in Your Application To ensure HyperTest can capture a wide range of scenarios, introduce some live traffic to your application. This can be done by simulating user activity or running existing test scripts that make API calls. The HyperTest SDK will record the requests made to downstream services, along with their responses. These recordings are crucial for creating accurate mocks that simulate real-world conditions during regression testing. 💡 Invest in 100% automation and let your developers focus on speedy releases while ensuring quality code. 4. Use HyperTest CLI to run the Test Mode Once the recording phase is complete, you can use the HyperTest CLI to replay the recorded requests. During this phase, the actual API calls will be replaced with the previously recorded mock responses. This allows you to test your application in a controlled environment, ensuring that any changes in your code do not break existing functionality. After running these tests, HyperTest generates a regression report that highlights any discrepancies or issues detected. 5. Use the Dashboard to View All the Regressions The final step is to access the HyperTest Dashboard, where you can view the detailed regression/coverage report. It provides a comprehensive evaluation of your test results, including pass/fail statuses, differences between expected and actual responses, and more. This visualization helps you quickly identify and address any regressions introduced during development, ensuring your application remains stable and reliable. Want to see it action for your services? Book a demo now Prevent Logical bugs in your databases calls, queues and external APIs or services Take a Live Tour Book a Demo

Discover how Trace IDs improve observability in distributed systems by enabling end-to-end request tracking, faster debugging, and performance optimization. 8 February 2025 7 Min. Read How Trace IDs enhance observability in distributed systems? WhatsApp LinkedIn X (Twitter) Copy link Pinpoint failures faster with HyperTest From Netflix's Edgar to Uber's Jaeger , major tech companies have developed their own tracing systems to gain better insights into their microservices architectures. In a microservices environment, requests often span multiple services. When developers need to debug or trace an issue, they require a way to uniquely identify each request as it moves through different services. To address this challenge, the distributed tracing pattern is employed. In this approach, each request is assigned a unique trace ID, which is then passed along to all the services that handle that request. Each service includes this trace ID in its log entries, allowing for comprehensive tracking and analysis. For instance, when a user initiates a request, the trace ID is propagated through all the services involved. This enables teams to visualize the entire request lifecycle, pinpointing where delays or errors occur. Discover how Zoop, Leena, and PayU cut incident response times by 50% using distributed tracing to automatically detect and alert developers about upstream service failures before deploying a PR. A little background on Trace IDs in Distributed Systems When you have a system that’s made up of many different services, understanding how they interact is key to maintaining performance and reliability. Trace IDs are the backbone of that understanding - Ben Stopford , Principal Engineer at Confluent In distributed systems, particularly those based on microservices, it is difficult to track requests among numerous services. As apps become larger, they have numerous interconnected services that communicate with one another over networks. This makes it challenging to monitor, debug, and optimize performance. ➡️ What Are Trace IDs? Trace IDs are unique identifiers assigned to individual requests as they traverse through various services in a distributed system. Each service that processes the request logs the trace ID along with relevant information, such as timestamps, processing times, and any errors encountered. This allows developers to follow the path of a request from its origin to its final destination, providing a comprehensive view of the request lifecycle. Breaking it down with a simple example👇 1️⃣ A Trace ID ( uuid4() ) is generated when a user places an order ( Trace ID Generation) import uuid import logging # Generate a unique Trace ID for the request trace_id = str(uuid.uuid4()) 2️⃣ This Trace ID is passed across services [Order → Payment → Inventory → Shipping] ( Propagation and Logging) # Simulate the Order Service def order_service(): logging.info(f"[Trace ID: {trace_id}] Order received") payment_service() # Simulate the Payment Service def payment_service(): logging.info(f"[Trace ID: {trace_id}] Processing payment") inventory_service() # Simulate the Inventory Service def inventory_service(): logging.info(f"[Trace ID: {trace_id}] Checking stock") shipping_service() # Simulate the Shipping Service def shipping_service(): logging.info(f"[Trace ID: {trace_id}] Scheduling delivery") # Start the request flow order_service() 3️⃣ If the order fails at any step, developers can trace logs using the Trace ID to find where the issue occurred. (Analysis) The Role of Trace IDs in Enhancing Observability Trace ids serve as unique identifiers for each request, allowing teams to track the flow of requests across various services. This capability is crucial for: ✅diagnosing issues, optimizing performance, and ensuring a seamless UX. Distributed tracing should be a first-class citizen in modern architectures. The key is ensuring Trace IDs aren’t just captured but also effectively propagated. - Adrian Cole, Creator of Zipkin 1️⃣ Visualizing Request Flows By aggregating logs from different services based on trace IDs, teams can visualize the entire request flow. This visualization helps in identifying bottlenecks, RCA and points of failure. We understand that even a minor code change can unintentionally break dependencies. And that’s where HyperTest’s distributed tracing feature comes into play. ✔️ It automatically identifies direct and indirect upstream services that would fail if a PR were deployed. ✔️ Instead of reacting to failures post-deployment, developers get real-time impact analysis based on live dependencies, ensuring that every change is validated across the entire service mesh before reaching production. ✔️ HyperTest auto-generates mocks while keeping Trace ID continuity, helping teams debug without live dependencies. Diagnose failures faster with HyperTest: trace every request path and service dependency. Try HyperTest now. 2️⃣ End-to-End Request Tracking Every request is assigned a unique Trace ID, linking all interactions across microservices. It enables seamless debugging by tracing how requests traverse through different services. Let’s take an example of an ecommerce app: A checkout process fails intermittently in an e-commerce app. Using Trace IDs, developers identify that the inventory service is taking too long to respond. Further analysis shows that a recent deployment introduced an inefficient database query, which is causing timeouts. 💡 Without Trace IDs , debugging this issue would take hours or days . With HyperTest’s distributed tracing capabilities , engineers can resolve it in minutes . ✔️ Captures and propagates Trace IDs across services like payment, inventory, and shipping. ✔️ Identifies the exact failing service (e.g., inventory service taking too long to respond). ✔️ Maps indirect upstream dependencies, revealing that a recent deployment introduced an inefficient database query causing timeouts. ✔️ Alerts developers before deployment if their new changes could potentially break dependencies in upstream or downstream services. 3️⃣ Comprehensive Logging and Monitoring With trace IDs, each service can log relevant information, such as processing times, errors, and other contextual data. This comprehensive logging is essential for monitoring system performance and diagnosing issues. function logEvent(message, traceId) { console.log(`[${traceId}] ${message}`); } app.get('/processPayment', (req, res) => { const traceId = req.traceId; logEvent('Starting payment processing', traceId); // Simulate payment processing logic const paymentSuccess = true; // Assume payment is successful if (paymentSuccess) { logEvent('Payment processed successfully', traceId); res.send('Payment successful'); } else { logEvent('Payment processing failed', traceId); res.status(500).send('Payment failed'); } }); Here’s how HyperTest will provide value instantly ( See it in action here ): Imagine an e-commerce checkout where a payment fails. Instead of sifting through scattered logs, you instantly see: The exact cart details and payment method used How the order request flowed through inventory, pricing, and payment services Which async operations, like order confirmation emails or fraud checks, were triggered The precise SQL queries executed for order validation and payment processing The payment gateway's response and any error codes returned 4️⃣ Performance Bottleneck Detection Measures latency at each service hop, identifying slow-performing components. Helps optimize service-to-service communication and database query efficiency. How Uber uses Trace IDs to debug production issues? Uber operates a massively distributed architecture, handling millions of rides daily. Initially, debugging slow API responses was challenging due to fragmented logs. By implementing Trace IDs within Jaeger (Uber’s open-source tracing tool) , Uber achieved: End-to-End Latency Tracking – Engineers could detect if a slowdown originated from the driver allocation service or payment gateway. Reduced MTTR (Mean Time to Resolution) – Debugging times dropped by 60% as Trace IDs linked logs across different microservices. Automated Bottleneck Detection – By leveraging Trace IDs, Uber’s system flagged services contributing to high p99 latencies. Conclusion Trace IDs are an indispensable tool in modern observability, enabling developers to correlate logs, analyze latencies, and identify bottlenecks across distributed systems. By leveraging Trace IDs effectively, engineering teams can reduce debugging time, improve performance insights, and enhance security . As distributed systems grow in complexity, the role of Trace IDs will only become more critical. ✔ Trace IDs provide a unique way to track requests across services. ✔ Adaptive tracing ensures critical traces are stored without performance overhead. ✔ Standardizing Trace ID formats prevent observability blind spots. ✔ Advanced use cases include A/B testing, AI-driven insights, and security monitoring. For teams looking to implement Trace IDs efficiently, adopting HyperTest can provide a strong foundation for distributed tracing along with automated test suite creation. Related to Integration Testing Frequently Asked Questions 1. What is a Trace ID in distributed systems? A Trace ID is a unique identifier assigned to a request, allowing developers to track its journey across multiple microservices. 2. How do Trace IDs improve debugging? Trace IDs link logs, metrics, and traces, helping engineers quickly pinpoint performance bottlenecks and failure points across services. 3. Can Trace IDs reduce system downtime? Yes, by enabling real-time issue detection and faster root cause analysis, Trace IDs help minimize downtime and improve system reliability. For your next read Dive deeper with these related posts! 08 Min. Read Using Blue Green Deployment to Always be Release Ready Learn More 08 Min. Read Generating Mock Data: Improve Testing Without Breaking Prod Learn More 13 Min. Read Understanding Feature Flags: How developers use and test them? Learn More

Unlock the power of contract testing in microservices. Explore clear contracts, comprehensive testing, automation, and CI/CD integration in our complete guide. 21 November 2023 07 Min. Read Contract Testing for Microservices: A Complete Guide Implement Contract Testing for Free WhatsApp LinkedIn X (Twitter) Copy link Everybody’s doing microservices now-a-days, but the teams keeps getting the same challenges over and over again and what they are really struggling with is: 💡 I switched to microservices to boost my business agility, but now I'm in a bit of a bind. I can make sure each piece of the puzzle works fine, but when it comes to ensuring the whole system runs smoothly, I'm kind of lost. Testing everything together ends up erasing most of the benefits of using microservices. It turns into a real headache – it's tough, expensive, and just plain annoying to set up. -Holly Cummins, Quarkus Companies, like Uber , even planned to switch from microservices to something they called “macroservices” due to the granular nature of microservices. Where the Testing Pyramid lacks when it comes to testing microservices? Initially, the testing pyramid worked well for testing monolith architecture. But when microservices came into picture, unit tests and E2E tests simply failed. Some major issues with the Martin Fowler’s pyramid are: ➡️Testing for System-Level Confidence: Component level tests are comfortable and fast to implement. But the confidence slowly fades away when the number of services, endpoints, and integrations keeps on growing when new features are implemented. While it's important to test individual components, the real test comes when everything is integrated. ➡️Hurdles with High-level testing: End-to-end tests are slow, prone to errors, hard to debug, and non-deterministic in general. Sometimes teams even avoid running them because of the high effort. They fail to data errors, and no one wants to spend time debugging them. It is not rewarding to debug and fix errors that are only related to the testing environment and not to the actual features of the system. And that’s where devs and QA folks started exploring other approaches for microservices testing . This is where contract testing came forward as a tailored solution for microservices. It offers a simpler and more manageable way to ensure these services talks and performs as decided. What is Contract Testing? Contract testing is a way to test integrations between services and ensure that all the integrations are still working after new changes have been introduced to the system. This allows for faster, more focused testing since only the interactions are tested. 💡 The main idea is that when an application or a service (consumer) consumes an API provided by another service (provider), a contract is formed between them. Contract testing breaks the testing boundaries between the services when compared to the component tests. This means that the services are no longer fully isolated from each other. The services are not directly connected either, like it happens with end-to-end tests. Instead, they are indirectly connected, and they communicate with each other using the contracts as a tool. How Does Contract Testing Works? Contract testing involves establishing and validating an agreement between two parties: the "Provider" (service owner) and the "Consumer" (service user). There are two main approaches to contract testing: consumer-driven and provider-driven. 👉 Consumer-driven Contract Testing: Consumer-driven contract testing is an approach where the consumer of a service takes the lead in defining the expected behavior of the service they depend on. They specify the contracts (expectations) that the service provider should meet, and then run tests to ensure that the provider complies with these contracts. This approach puts consumers in control of their service dependencies and helps prevent unexpected changes or regressions in the service they rely on. 👉 Provider-driven Contract Testing: Provider-driven contract testing is initiated by the service provider. In this approach, the provider defines the contracts that consumers should adhere to when interacting with the service. The provider sets the expectations and provides these contracts to consumers. Consumers, in turn, run tests to ensure that their usage of the service complies with the contracts specified by the provider. This approach allows the service provider to have a more proactive role in maintaining the integrity and stability of the service. 👉 Working of Consumer-driven Contract Testing: ➡️The contract contains information about how the consumer calls the provider and what is being used from the responses. ➡️As long as both of the parties obey the contract, they can both use it as a basis to verify their sides of the integration. The consumer can use it to mock the provider in its tests. ➡️The provider, on the other hand, can use it to replay the consumer requests against its API. This way the provider can verify that the generated responses match the expectations set by the consumer. ➡️With consumer-driven contracts, the provider is always aware of all of its consumers. This comes as a side product when all the consumers deliver their contracts to the provider instead of consumers accepting the contracts offered by the provider. Benefits of Contract Testing ➡️Maintenance: They are easier to maintain as you don't need to have an in-depth understanding of the entire ecosystem. You can write and manage tests for specific components without being overwhelmed by the complexity of the entire system. ➡️Debugging and Issue Resolution: Contract tests simplify the debugging process. When a problem arises, you can pinpoint it to the specific component being tested. This means you'll often receive precise information like a line number or a particular API endpoint that is failing, making issue resolution more straightforward. ➡️Local Bug Discovery: Contract tests are excellent at uncovering bugs on developer machines during the development process. This early detection helps developers catch and fix issues before pushing their code, contributing to better code quality and reducing the chances of defects reaching production. ➡️ Integration Failures: If discrepancies arise during either phase of Contract testing, it signals a need for joint problem-solving between the Consumer and Provider. Use-cases of Contract Testing Contract testing is a useful way to make sure microservices and APIs work well together. But it's not the best choice for all testing situations. Here are some cases where contract testing is a good idea: ✅ Microservices Communication Testing: Use Case: In a microservices architecture, different services need to communicate with each other. Contract testing ensures that these services understand and meet each other's expectations. # Consumer Service Contract Test def test_consumer_service_contract (): contract = { "request": {"path": "/user/123", "method": "GET"}, "response": {"status": 200 , "body": {"id": 123 , "name": "John"}} } response = make_request_to_provider_service(contract) assert response.status_code == 200 # Provider Service Contract Implementation def make_request_to_provider_service (contract): # Code to handle the request and provide the expected response pass ✅API Integration/ Third-Party Integration Testing: Use Case: When integrating with external APIs, contract testing helps ensure that the application interacts correctly with all the third-party APIs, preventing any compatibility issues and ensure that the code is reliable and secure. # Contract Test for External API Integration def test_external_api_contract (): contract = { "request": {"path": "/products/123", "method": "GET"}, "response": {"status": 200 , "body": {"id": 123 , "name": "Product A"}} } response = make_request_to_external_api(contract) assert response.status_code == 200 # Code for Making Requests to External API def make_request_to_external_api ( contract): # Code to send the request to the external API and handle the response pass ✅ UI Component Interaction Testing: Use Case: Contract testing can be applied to UI components, ensuring that interactions between different parts of a web application, like front-end and back-end, work as expected. // Front-End UI Component Contract Test it ('should display user data when provided', () => { const userData = { id: 123, name: 'Alice' }; const component = render ( < UserProfile data ={userData} /> ); const userElement = screen. getByText ( /Alice/ ); expect (userElement) . toBeInTheDocument (); }); // Back-End API Contract Implementation app. get ('/api/user/123', ( req, res ) => { res. status ( 200 ). json ({ id: 123 , name: 'Alice' }); }); Difference Between Contract Testing and Integration Testing Contract testing and integration testing are sometimes mistaken for one another; despite sharing a common end goal, they diverge significantly in their approaches. Without contract testing, the only way to ensure that applications will work correctly together is by using expensive and brittle integration tests. Pact.io introduction Contract testing Integration testing Scope Focuses on verifying interactions at the boundary of an external service. It ensures that a service's output conforms to certain expectations and that it correctly handles input from another service. The primary concern is the "contract" or agreement between services. Addresses the interaction between integrated components or systems as a whole. It checks if different components of an application work together as expected. Test Depth Is concerned with the correctness of interactions, not the internal workings of each service. It verifies if a service lives up to its "contract" or promised behavior. Goes beyond just interactions. It can dive deep into the integrated system's logic, ensuring not only correct data exchange but also correct processing. Test Maintenance Contracts can be stable and act as a form of documentation. If teams adhere to the contract, changes in implementation shouldn't necessitate changes in the test. Tends to be more brittle. A change in one service can often break integration tests, especially if they are end-to-end in nature. Isolation Uses mocked providers, allowing for testing in isolation. A consumer can be tested against a contract without having a running instance of the provider and vice versa. Requires a more integrated environment. To test the interaction between two services, both services usually need to be up and running. Feedback Loop Provides quicker feedback since it tests against contracts and doesn't require setting up the entire integrated environment. Might have a slower feedback loop, especially for end-to-end tests, because they can be more extensive and require more setup. Purpose Aims to give confidence that services fulfill their promises to their consumers. It's particularly useful in a microservices environment where teams develop services independently. Ensures that when different components come together, they operate harmoniously. It's crucial for catching problems that might not show up in unit or contract tests. Contract testing is about adhering to agreements between services, while integration testing is about achieving seamless operation when components are combined. Both types of testing are complementary and important in their own right. Tools to Perform Contract Testing Numerous tools exist in the market for conducting contract testing on APIs and microservices. Pact and Spring Cloud Contract are two of the most prominent tools in the realm of contract testing, widely recognized for their specific features and capabilities: Let’s discuss the two of them: ➡️ PACT: PACT stands out in the contract testing domain for its user-friendly interface and flexibility across various programming languages. Pact enables both consumers and providers to define and verify their contracts effectively. It operates on a consumer-driven approach, meaning the consumer code generates the contract. This approach encourages consumer specifications to be fully considered in the contract, ensuring that the provided service meets these requirements. Pact’s versatility extends to support for numerous programming languages, including Ruby, JavaScript, and JVM languages, catering to a wide range of development environments. Here’s a complete guide on how PACT contract testing works and where it still falls short. ➡️SPRING CLOUD CONTRACT: This tool is specifically tailored for Java applications, particularly those developed using the Spring Boot framework. Unlike Pact, Spring Cloud Contract adopts a provider-driven approach. It allows providers to define and verify their contracts, using Groovy or YAML for contract descriptions. This tool excels in automating the generation of test cases from these contracts, seamlessly integrating these tests into the build process. This integration ensures that any changes in the service are immediately tested against the contract, thereby maintaining consistency and reliability in service interactions. Both Pact and Spring Cloud Contract offer unique approaches to contract testing, catering to different methodologies and programming environments. The choice between them often depends on the specific needs of the project, the preferred development approach (consumer-driven vs. provider-driven), and the primary programming language used within the team. We've covered PACT in detail here; head over now to get more insightful information here . Check out our other contract testing resources for a smooth adoption of this highly agile and proactive practice in your development flow: Tailored Approach To Test Microservices Comparing Pact Contract Testing and HyperTest Checklist For Implementing Contract Testing Related to Integration Testing Frequently Asked Questions 1. What is contract testing in microservices? In the realm of microservices, contract testing guarantees dependable communication among services. Each service establishes a set of contracts outlining anticipated interactions. Through testing, compliance with these contracts is verified, allowing for autonomous development and deployment while upholding system integrity. 2. What type of testing is a contract test? Contract testing falls under the category of "consumer-driven contract testing," where service consumers specify expected interactions (contracts). Tests are then conducted by both service providers and consumers to ensure compliance with these contracts, promoting compatibility and reliability in microservices architecture. 3. What are the best practices for contract testing? Best practices for contract testing in microservices include defining clear and concise contracts, ensuring comprehensive test coverage, automating tests for efficiency, maintaining version control for contracts, and integrating contract testing into the continuous integration pipeline. Collaborative communication between service providers and consumers, along with regular contract updates, promotes system reliability. Additionally, documenting contracts and test results facilitates understanding and troubleshooting. For your next read Dive deeper with these related posts! 09 Min. Read Top Contract Testing Tools Every Developer Should Know in 2025 Learn More 14 Min. Read PACT Contract Testing: A Step-by-Step Guide Learn More 04 Min. Read Contract Testing: Microservices Ultimate Test Approach Learn More

Learn to build an API regression suite and get insights about why the most powerful Regression Technique works. 6 June 2024 03 Min. Read API Regression Suite: Effective Technique & Benefits WhatsApp LinkedIn X (Twitter) Copy link Get the Guide With APIs carrying the majority of the functional and business logic for applications, teams use a variety of open source and in-house tools for testing APIs but struggle to catch every possible error. There is a way to catch every error , every critical regression in your APIs without writing a single line of code. Why do existing regression techniques fail? The hardest thing about writing API or backend tests is accurately defining the expected behavior. With 80%+ of the web or mobile traffic powered by APIs, all new features in applications involve a corresponding update or change in relevant APIs. These changes would be of two types, desired i.e. ones that are intended , and undesired i.e. the ones that might break the application as side-effects and result in bugs . It is hardest to find these side-effects or regression issues because unless one asserts every single validation across all the APIs, new changes will break some unasserted validation, causing an unknown bug. To ensure the expected behavior of applications remains intact forever means anticipating and testing every new change, which becomes harder to impossible as the number of APIs increases and becomes more complex. The Solution API changes that can cause application failures would because of either: Contract or schema changes Data validation issues or simply Status code failures The best test strategy is the one that reports all changes across all updated APIs in the new build. However, as applications grow and involve more APIs, covering and testing all new changes becomes increasingly difficult. The simplest way to catch deviance from expected behavior in APIs is to compare them with the version that is stable or currently live with users. The existing version of the API or application that is currently live with users is the source of truth. Any deviance from how the application currently works (expected) is going to become a bug or problem (unexpected). Summing it Up with HyperTest A regression suite that compares responses across the 2 versions for the same user-flow is the surest way to ensure no breaking change has happened, and the deviance in response is the only possibility of any breaking change. HyperTest is the only solution you need to build an API regression suite . It is a no-code autonomous API testing tool that generates tests automatically based on the real network traffic. Its data-driven testing approach makes sure to run contract[+data] tests that never let you miss any API failure again. If you're worried about leaking bugs to production, HyperTest can help mitigate those concerns. By using the first-of-its-kind HyperTest platform, you can rigorously test your APIs and Microservices. To learn more or request a demo, please visit https://hypertest.co/ . Frequently Asked Questions 1. What is API regression testing? API regression testing is a type of software testing that ensures that new code changes in an API do not introduce regressions, i.e., unintended side-effects that may break existing functionality or cause new bugs. 2. Why do traditional regression testing methods fail? Traditional regression testing methods often fail because they may not cover every possible validation across all APIs, leading to potential unknown bugs when unasserted validations are broken by new changes. 3. How does HyperTest address the challenges of API regression testing? HyperTest addresses these challenges by providing a no-code, autonomous API testing tool that automatically generates tests based on real network traffic, ensuring that all contract and data validations are tested. For your next read Dive deeper with these related posts! 10 Min. Read Top 10 API Testing Tools in 2025: A Complete Guide Learn More 08 Min. Read What is API Test Automation?: Tools and Best Practices Learn More 07 Min. Read Top 6 API Testing Challenges To Address Now Learn More

Katalon has been a segment's favorite for its easy-to-use functionality, but it's high time to make sure your APIs are also functional and tested properly with HyperTest. 17 September 2024 06 Min. Read Common Challenges in API Testing with Katalon WhatsApp LinkedIn X (Twitter) Copy link Explore API Test Automation Tool Let’s be honest—API testing isn’t always smooth, even with popular tools like Katalon. Katalon Studio has become a popular tool for API testing due to its: ease of use, out-of-the-box functionality, and support for multiple automation frameworks. While Katalon offers a user-friendly interface and strong features, it’s not without its pain points However, like any tool, Katalon has its own limitations and challenges specific to API testing. Let's dive into some of the most common challenges and, more importantly, how to overcome them. Let’s get started: 1. Limited Flexibility with Assertions The Problem: While Katalon offers built-in assertions for checking API response codes and simple key-value pairs, it lacks advanced features for handling complex response structures like deeply nested JSON or XML objects. For more sophisticated validations, such as dynamic content in API responses, the built-in assertions might not be enough. Why It’s a Problem: When testing APIs that return large, deeply nested JSON objects or complex XML structures, you might need to perform validations on multiple levels or verify dynamic data that changes with each call. Katalon’s assertion methods often require custom scripting, which defeats the purpose of using a tool designed to reduce manual coding. How can you solve this? HyperTest, with its AI-driven automation, can automatically generate and validate complex response structures without the need for extensive custom scripting. This reduces the time spent on manual test creation and simplifies handling complex data types. 2. Poor Handling of Dynamic Data in Requests The Problem: Many APIs require dynamic data inputs—like tokens, timestamps, or user IDs—that change frequently or vary with each test run. While Katalon provides some capabilities to manage dynamic data through test variables, it lacks seamless solutions for handling dynamic data across a suite of API tests. Why It’s a Problem: Users often struggle to automate API tests where inputs need to be updated dynamically from one test to another, such as refreshing authentication tokens or passing IDs retrieved from a previous API call. In such cases, you might need to write custom scripts to automate this process, which can increase complexity and maintenance overhead. How can you solve this? HyperTest excels at managing dynamic data like tokens, session IDs, and time-sensitive parameters. It can generate integration tests based on real-time traffic, meaning you don’t need to constantly update or refresh data manually as you might with Katalon. This minimizes the maintenance burden. 3. Complex Authentication Handling The Problem: Although Katalon supports basic authentication mechanisms like Basic Auth and API keys , handling more complex authentication flows—like OAuth 2.0 , JWT tokens , and SAML —is not as straightforward. The tool lacks built-in support for automating the entire authentication flow (e.g., retrieving tokens, refreshing them, and injecting them into subsequent requests). Why It’s a Problem: APIs that rely on OAuth or JWT-based authentication require continuous updates to tokens or session IDs. Katalon users often need to create custom scripts to fetch tokens manually, refresh them, and pass them into API headers. While Katalon supports chaining API requests, the process of automating this across multiple test cases can be cumbersome, especially when dealing with frequent token expiration. How can you solve this? For complex authentication mechanisms such as OAuth or JWT, HyperTest streamlines the process by automatically managing token generation and handling session states. This eliminates the need for custom scripts or manual token updates, which can be tedious in Katalon. 4. Steep Learning Curve for Advanced Customization The Problem: Katalon is marketed as a tool for non-developers and manual testers transitioning to automation, but its flexibility is limited for more advanced API testing scenarios. Once you get beyond the basics, the tool requires significant scripting in Groovy to handle complex test cases, making it less accessible for non-technical users. Why It’s a Problem: Teams that adopt Katalon for its ease of use often find themselves grappling with Groovy scripting when they need to implement more advanced features like custom assertions, API response parsing, or request chaining with dynamic data. For testers without coding skills, this can be a significant barrier, making them dependent on developers to write or troubleshoot scripts. How can you solve this? Developers' set-up our SDK (2-lines) across any of their (backend) services and configure it to record traffic from any environment. When HyperTest works in RECORD mode it collects end to end trace of every incoming request i.e. the request, response and outbound calls. These requests (tests) can be replayed on a new build later to check for regressions in API responses and outbound calls. In the REPLAY mode HyperTest uses mocked responses of all dependent systems to keep tests non-flakey and results deterministic and consistent. HyperTest (2-line SDK) auto-instruments all key functions and methods across all libraries you can use to make outbound calls. This helps HyperTest mock these calls in REPLAY without asking developers to make any change in their source code. Final Verdict We at HyperTest are trying to make integration testing easy for developers. A lot of other teams and tools like Katalon have taken a stab at this problem and having seen them all we believe we have built an approach that helps developers achieve this with minimum effort and pain. Watch this demo and see how we're making things easy for devs: Make sure to check-out our other content assets in this series, it will help you in deciding why HyperTest and why not "Katalon": ➡️ Top 5 Katalon Alternatives and Competitors ➡️ Katalon vs HyperTest: The Right API Testing Tool For You Frequently Asked Questions 1. How can I overcome the limitations of Katalon's built-in assertions for complex API responses? To address the limitations of Katalon's built-in assertions, you can explore third-party libraries or custom scripting options. Or HyperTest, it creates dynamic assertions without you missing any sort of regressions. 2. What are some effective strategies for handling dynamic data in API requests? You can use techniques like parameterization, data-driven testing, or environment variables to manage dynamic data in API requests. This helps ensure that your test cases remain up-to-date and adaptable to changing data. Or HyperTest to explore a no-test data preparation setup itself. 3. What are some tips for integrating Katalon with other testing tools or CI/CD pipelines? Leverage Katalon's built-in integration capabilities and follow best practices for configuring and maintaining integrations. Consider using community resources and documentation for guidance on specific tools and scenarios. For your next read Dive deeper with these related posts! 09 Min. Read The Ultimate Guide to API Testing with Katalon Learn More 06 Min. Read Katalon vs HyperTest: The Right API Testing Tool For You Learn More 11 Min. Read Top 5 Katalon Alternatives and Competitors Learn More

Delve into HyperTest’s Resource Library to elevate your coding prowess. Access a wealth of developer resources including guides, checklists, whitepapers, and exclusive event content. Stay ahead of the curve with insights from the 2023 Regression Trends report and real-world success stories. Resource Library Write cleaner, bug-free code faster with our developer resources (guides, checklists, whitepapers & events). State of API Testing Report: Regression Trends 2023 Drawing insights from our own product analytics for the entire year 2023, the report reveals a staggering 8,661,895 regressions & 8K+ test runs reveal 2024's must-know goals, challenges, and trends. Download the Report Customer Spotlight: Real-World Results Airmeet and HyperTest: A Partnership to Erase 70% Outdated Mocks and Enhance Testing Speed By 80% Read it now How Cashify Achieved Over 90% Code Coverage in Days with HyperTest Read it now How Yellow.ai Employs HyperTest to Achieve 95% API Coverage and Ensure a Flawless Production Environment Read it now Processing 1.5 Million Orders, Zero Downtime: How Nykaa Optimizes with HyperTest Read it now Half a Million Users, Zero Schema Failures: How Fyers Leverages Hypertest Read it now Events Mock APIs Message Queues and Databases in One Place Mocking Watch Now 29 January 2025 09.00 AM EST, 7:30 PM IST Implementing TDD: Organizational Struggles & Fixes Best Practices Watch Now 18 December 2024 09.00 AM EST, 7:30 PM IST Get to 90%+ coverage in less than a day without writing tests Best Practices Watch Now 28 November 2024 09.00 AM EST, 7:30 PM IST Build E2E Integration Tests Without Managing Test Environments or Test Data Best Practices Watch Now 13 November 2024 09.00 AM EST, 7:30 PM IST No more Writing Mocks: The Future of Unit & Integration Testing Unit Testing Watch Now 6 October 2024 10.00 AM EDT, 7:30 PM IST Ways to tackle Engineering Problems of High Growth Teams Best Practices Watch Now 30 May 2024 10.00 AM EDT, 7:30 PM IST Zero to Million Users: How Fyers built and scaled one of the best trading app Best Practices Watch Now 20 March 2024 09.00 AM EST, 7:30 PM IST Masterclass on Contract Testing: The Key to Robust Applications Contract Testing Watch Now 28 February 2024 09.00 AM EST, 7:30 PM IST Why Clever Testers Prioritize API Testing Over UI Automation API Testing Watch Now 8 January 2024 09.00 AM EST, 7:30 PM IST How to do End-to-End testing without preparing test data? E2E Testing Watch Now 30 November 2023 10.00 AM EDT, 7:30 PM IST What no one will tell you about using GenAI for Testing GenAI for Testing Watch Now 25 October 2023 10.00 AM EDT, 7:30 PM IST Whitepapers All Blogs 05 Min. Read What is Mockito Mocks: Best Practices and Examples Learn More 04 Min. Read Contract Testing: Microservices' Ultimate Test Approach Learn More 05 Min. Read Boost Dev Velocity with Automated Integration Testing Learn More 10 Min. Read How To Implement Shift Left Testing Approach Learn More 10 Min. Read How To Implement Shift Left Testing Approach Learn More 12 Min. Read Common Types of Bugs in Software Testing Learn More 13 Min. Read The Most Comprehensive ‘How to use’ Postman Guide for 2024 Learn More 08 Min. Read Microservices Testing Challenges: Ways To Overcome Learn More 07 Min. Read Contract Testing for Microservices: A Complete Guide Learn More 07 Min. Read Regression Testing for FinTech Apps Learn More 07 Min. Read Testing Pyramid: Why won’t it work for microservices testing? Learn More 09 Min. Read How to perform Mobile App API Testing Learn More 04 Min. Read Online Postman Tool for API Testing Vs HyperTest Learn More Subscribe to TDD Monthly, Our monthly Newsletter Subscribe Now