Discover what feature flags are and why developers use them to enable safer rollouts, faster releases, and real-time control over application features. 3 December 2024 13 Min. Read Understanding Feature Flags: How developers use and test them? WhatsApp LinkedIn X (Twitter) Copy link Test Flags Easily Without Environment Setup Let’s get started with a quick story: Imagine you’re a developer, and you’ve shipped out a new feature after testing it well. You sigh a moment of relief. But too soon when you start see your PagerDuty console or Prometheus alert manager buzzing with unexpected spikes in error rates, endpoint failures and container crashes. What is going wrong? Now you doubt if you tested this new feature enough, if you missed an edge case or an obvious enough scenario in the hurry to get the feature live. But you tested it thoroughly once locally before committing, and then again when you raised the PR. How did you miss these obvious failures? Oh, that’s the issue. The real test of a new feature is front of real users who use the app in different, unthinkable ways, hard to replicate in a controlled environment like dev or ‘stage’. Besides the actual deployment of a new (maybe incomplete) feature can be minimised if it is released to a smaller group of users over everyone-at-once which delivers real feedback without the impending risk. So, what’s the solution here? Feature flags originated as a solution to several challenges in software development, especially in the context of large, complex codebases. In traditional development, new features could only be developed in separate branches and merged when complete, leading to long release cycles. This created bottlenecks in the development process and sometimes even introduced risks when deploying large changes. What are Feature Flags? Feature flags are conditional statements in code that control the execution of specific features or parts of a system. They allow developers to turn features on or off dynamically without changing the underlying code. Flags can be applied to: New Features : Enabling or disabling new functionality during development or A/B testing. Release Control : Gradually rolling out features to users (e.g., for canary releases). Performance Tuning : Toggling between performance configurations or optimizations. Security : Disabling certain features during security incidents or emergency fixes. How does a Feature Flag look like? A Feature Flag is typically implemented as a conditional check in the code, which determines whether a specific feature or behavior should be enabled or disabled. Simple example of a feature flag: boolean isNewFeatureEnabled = featureFlagService.isFeatureEnabled("new-feature"); if (isNewFeatureEnabled) { // Execute code for the new feature System.out.println("New feature is enabled!"); } else { // Execute legacy behavior System.out.println("Using the old feature."); } How a complex feature flag looks like? Feature flags can also be more complex, such as targeting a specific group of users or gradually rolling out a feature to a percentage of users. let user = getUserFromContext(); if (featureFlagService.isFeatureEnabledForUser("new-feature", user)) { // Activate feature for specific user console.log("Welcome, premium user! Here's the new feature."); } else { // Show default behavior console.log("Feature is not available to you."); } The flag is essentially a key-value pair, where the key represents the name of the feature and the value dictates whether it's active or not. Who uses feature flags? Feature flags are integrated directly into the code, so their setup requires a development or engineering team to configure them within the application. Consequently, software developers are often the primary users of feature flags for controlling feature releases. ✅ They also facilitate A/B testing and experimentation, making it possible to test different versions of a feature and make data-driven decisions. ✅ Gradual rollouts allow features to be released to internal users, then beta testers, and finally everyone, with the option to quickly toggle the feature off if issues arise. ✅ Feature flags enable developers to work directly in the main branch without worrying about conflicts, reducing merge headaches. ✅ They also optimize CI/CD workflows by enabling frequent, small deployments while hiding unfinished features, minimizing the risks associated with large, infrequent releases. What results can devs in FinTech achieve by using feature flags? We’re specifically talking about the banking apps here since those apps hinges on fast, reliable, and safe software delivery, but many banking institutions are slow to change, not because of a lack of motive, but because archaic infrastructure and legacy code stand in the way. Companies like Citibank and Komerční Banka have successfully updated their systems by using feature flags to ensure security and smooth transitions. Komerční Banka releases updates to non-production environments twice a day and has moved 600 developers to its New Bank Initiative. Alt Bank shifted from a monolithic system to microservices and continuous deployment, connecting feature flags to both their backend and mobile app. Rain made it easier for their teams by removing the need to manually update configuration files. Now, they can control user segments and manage feature rollouts more easily. Vontobel increased development speed while safely releasing features every day. How Feature Flags Function? Toggle at Runtime : Feature flags act as switches in your code. You can check if a flag is enabled or disabled and then decide whether or not to execute certain parts of the code. It's like adding a conditional if check around a feature you don’t want to expose yet. Dynamic Control : Flags can be managed externally (e.g., via a dashboard or config file) so they can be flipped without deploying new code. Granular Rollouts : Feature flags can be set per-user, per-region, or even per-application version. You can roll out a feature to a small subset of users or to all users in a specific region. Remote Flags : Some flags can be controlled remotely, using a feature flag service or API. This lets teams update flags without needing to touch the code. Flags as Variables : Under the hood, flags are just boolean variables (or maybe more complex types, like integers or strings). They're checked at runtime to control behavior—just like how environment variables work for config, but with the added flexibility of toggling things at runtime. Gradual Rollout : Instead of flipping a feature on for everyone all at once, you can roll it out incrementally—first for internal devs, then beta testers, then a few power users, and eventually, the entire user base. This reduces risk by catching issues early, before the feature goes full-scale. This means less downtime, fewer bugs in production, and faster iterations . Feature flags are like cheats for managing releases—flexible, fast, and low-risk. Top 5 Tools for Feature Flag Services Feature flags are crucial tools for managing feature deployment and testing in modern development environments. Let’s discuss the top 5 feature flag services to help you get started with: Feature LaunchDarkly Split.io Flagsmith Unleash Optimizely Ease of Setup Easy, with quick integration Easy for small projects, moderate for enterprise Moderate, documentation varies Can be complex due to open-source nature Straightforward for experienced teams User Interface Highly intuitive and user-friendly Clean, but can be confusing for new users Functional but lacks polish Basic, less intuitive Polished and user-focused Custom Rule Capabilities Highly flexible with custom rules Good, but less flexible than LaunchDarkly Limited to simple rules Mostly basic, some advanced features in paid versions Very sophisticated, great for complex setups Client-Side Performance Very efficient, minimal latency Efficient, with good SDK performance Moderate, depending on setup Can vary, self-hosting impacts performance High-performance, especially in mobile environments Adaptability to Complex Environments Best for highly dynamic environments Good, requires some custom setup Not ideal for very complex setups Varies with installation Excellent for multi-platform environments Scalability Handles scaling seamlessly Scales well, some planning needed Can struggle in large-scale implementations Scaling can be challenging in self-hosted Designed for large-scale enterprises Update Frequency Constant updates with new features Regular updates, sometimes slower Infrequent updates, depends on community Infrequent, open-source pace Regular, innovation-focused updates LaunchDarkly LaunchDarkly offers powerful real-time updates, granular targeting, robust A/B testing, and extensive integrations. It’s ideal for large teams with complex deployment needs and supports a full-feature lifecycle. Pricing : Subscription-based with custom pricing depending on usage and team size. Split.io Split.io excels in feature experimentation with A/B testing, detailed analytics, and easy-to-use dashboards. It integrates well with popular tools like Datadog and Slack and supports gradual rollouts. Pricing : Subscription-based, with custom pricing based on the number of flags and users. Flagsmith Flagsmith is open-source, providing the flexibility to self-host or use its cloud-hosted version. It supports basic feature flagging, user targeting, and simple analytics, making it ideal for smaller teams or those wanting more control. Pricing : Freemium model with a free tier and subscription-based plans for larger teams. Unleash Unleash is an open-source tool that offers full flexibility and control over feature flagging. It has a strong developer community, supports gradual rollouts, and can be self-hosted to fit into any tech stack. Pricing : Open-source (self-hosted, free), with premium support and cloud-hosted options available for a fee. Optimizely Optimizely is robust for feature experimentation and A/B testing, with excellent support for multivariate testing. It provides advanced user targeting and detailed analytics, making it a good choice for optimizing user experiences. Pricing : Subscription-based, with custom pricing depending on the scale of experimentation and features required. Why Testing Feature Flags are crucial? Testing feature flags is absolutely crucial because, without it, there’s no way to ensure that toggles are working as expected in every scenario. Devs live in a world of multiple environments, users, and complex systems, and feature flags introduce a layer of abstraction that can break things silently if not handled properly. Imagine pushing a new feature live, but the flag’s logic is broken for certain user segments, leading to bugs only some users see, or worse, features that should be hidden are exposed. You can’t afford to let these flags slip through the cracks during testing. Automated tests are great, but they don’t always account for all the runtime flag states, especially with complex rules and multi-environment setups. Feature flags need to be thoroughly tested in isolation and within the larger workflow—checking flag toggling, multi-user behavior, performance impact, and edge cases. If a flag is misbehaving, it can mean the difference between smooth rollouts or catastrophic rollbacks. Plus, testing feature flags helps catch issues early—before they make it to production and cause unplanned downtime or customer frustration. In short, feature flags might seem simple but testing them is just as important as testing the features they control. Problems with Testing Feature Flags Testing feature flags can be a real pain in the neck. ✅ For one, there’s the issue of environment consistency —flags might work perfectly in staging but fail in production due to differences in user data, network conditions, or backend services. ✅ Then, there’s the complexity of flag states —it’s not just about whether a flag is on or off, it’s about testing all possible combinations, especially when dealing with multiple flags interacting with each other. If flags are linked to user-specific data or settings (like targeting only a subset of users), testing each permutation manually can quickly spiral out of control. The Current State of Testing Feature Flags Currently, feature flags are being tested through a mix of unit tests (to check flag states in isolated components), integration tests (to ensure flags interact correctly across services), and E2E testing (to simulate real-world flag scenarios). But it’s often a manual setup at first, before implementing tools like LaunchDarkly , Split.io , or custom testing frameworks. Some teams write mocking tools to simulate different flag states, but these can get out of sync with the actual feature flag service. ➡️ Since states are involved here, manual testing is the most common way to test the toggling nature of these feature flags. But it is prone to errors and can’t scale. Devs often end up toggling flags on and off, but unless there's solid automation to verify those states under various conditions, things can easily break when flags behave differently across environments or after an update. Also, you can't always trust that a flag toggle will always trigger the expected behavior in edge cases (like race conditions or service outages). ➡️ Some devs rely on feature flag testing frameworks that automate toggling flags across test scenarios, but these are often too generic or too complex to fit the specific needs of every app. ➡️ End-to-end (E2E) testing is useful but can be slow, especially with dynamic environments that require flag values to be tested for different users or groups. Another challenge is testing the fallback behavior —when flags fail, do they default gracefully, or do they bring down critical features? Ultimately, testing feature flags properly requires continuous validation, automated checks for each flag change, across different segments, environments, and use cases. The Right Test Strategy for Teams working with Feature Flags Many people mistakenly believe they must test every possible combination of feature flags in both on and off states. This approach quickly becomes impractical due to the sheer number of combinations. In reality, testing every flag combination isn't necessary—or even possible. Instead, focus on testing a carefully selected set of scenarios that cover the most important flag states. Consider testing these key flag combinations: Flags and settings currently active in production Flags and settings planned for the next production release, including combinations for each new feature States that are critical or have caused issues in the past ✅ Testing in production We all know unit tests and integration/E2E tests comes pretty handy for testing feature flags, but they all come with their own set of limitations. So, here we are going to discuss one workable approach that eliminates the need for you to: ➡️ prepare test data for testing each possible combination of feature flag “on” and “off” stage ➡️ manage multiple environments, when you can reap the maximum benefits when you’re testing in production ➡️ testing in isolation, when you can test with the real traffic your application gets to get more confidence with your feature states Let's discuss the approach in detail: The best way to test feature flags is to test them naturally alongside your regular code testing. This involves a record and replay approach where you set up your services with the solution SDK in your production environment (which receives real traffic, leading to higher confidence). The SDK records all incoming requests to your app and establishes them as a baseline response. This recorded version automatically captures all interactions between your services, database calls, and third-party API communications. Here's how the testing works: Let's say you've created two new feature flags that need testing. The SDK records a new version of your app with all the changes and compares it with the baseline version. It not only identifies discrepancies between versions but also helps you understand how your feature flags affect the user journey. This approach is both fast and scalable across multiple services: Services don't need to remain active during testing Workflows can be recorded and tested from any environment All code dependencies are automatically mocked and updated by the system This approach is ideal for gaining confidence and getting instant feedback that your code will work correctly when integrating all components together. Major e-commerce companies like Nykaa and Purplle, which rely heavily on feature flags, are successfully using this approach to maintain stable applications. ✌️ Simulate Real-World Conditions ✌️ Test Flag Combinations and Interactions using Integration tests ✌️ Automate Flag Testing with Continuous Integration Do these goals align with what you want to achieve? If so, share your details with us , and we'll help you implement seamless feature flag testing. Conclusion When you’re working with feature flags, it is pretty obvious that you must be maintaining staging environments. But the problem occurs when the tested built is passed on to the prod environment and there it reports bugs or errors. And that’s true also, since there are “n” of conditions under each feature flag which can’t be tested properly in staging, as seeding and preparing test data to cover all the scenarios and edge cases is also a challenge in itself. Hence, a smart testing approach that tests the source code of feature flags naturally with the real traffic can be one solution to come out of this problem. Schedule A Demo Now Related to Integration Testing Frequently Asked Questions 1. What is a feature flag in software development? A feature flag is a tool that lets developers enable or disable features in an application without deploying new code. 2. Why do developers use feature flags? Feature flags simplify experimentation, enable safer rollouts, and accelerate development by separating deployment from feature releases. 3. How do feature flags improve debugging? Feature flags allow developers to deactivate faulty features instantly, reducing downtime and simplifying issue isolation. For your next read Dive deeper with these related posts! 07 Min. Read All you need to know about Apache Kafka: A Comprehensive Guide Learn More 08 Min. Read Using Blue Green Deployment to Always be Release Ready Learn More 09 Min. 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Unit vs. Functional Testing: Know the Difference! Master these testing techniques to ensure high-quality software. Focus on code units vs. overall app functionality. 16 July 2024 07 Min. Read Difference Between Functional Testing And Unit Testing WhatsApp LinkedIn X (Twitter) Copy link Checklist for best practices Ensuring a product functions flawlessly is a constant battle in this fast-moving development cycles today. Developers, wield a powerful arsenal of testing techniques. But within this arsenal, two techniques often cause confusion: unit testing and functional testing. This blog post will be your guide, dissecting the differences between unit testing and functional testing . We'll unveil their strengths, weaknesses, and ideal use cases, empowering you to understand these crucial tools and wield them effectively in your software development journey. What Is Functional Testing? Functional testing is a type of software testing that focuses on verifying that the software performs its intended functions as specified by the requirements. This type of testing is concerned with what the system does rather than how it does it. Functional testing involves evaluating the system's operations, user interactions and features to ensure they work correctly. Testers provide specific inputs and validate the outputs against the expected results. It encompasses various testing levels, which includes system testing , integration testing and acceptance testing. Functional testing often uses black-box testing techniques , where the tester does not need to understand the internal code structure or implementation details. When comparing unit testing vs. functional testing, the primary distinction lies in their scope and focus. While unit testing tests individual components in isolation, functional testing evaluates the entire system's behaviour and its interactions with users and other systems. What is Unit Testing? Unit testing is a software testing technique that focuses on validating individual components or units of a software application to ensure they function correctly. These units are typically the smallest testable parts of an application, such as functions, methods, or classes. The primary goal of unit testing is to isolate each part of the program and verify that it works as intended, independently of other components. Unit tests are usually written by developers and are run automatically during the development process to catch bugs early and facilitate smooth integration of new code. By testing individual units, developers can identify and fix issues at an early stage, leading to more maintainable software. Unit tests serve as a form of documentation, illustrating how each part of the code is expected to behave. Unit Testing vs. Functional Testing: How Do They Work? Unit testing and functional testing serve distinct purposes in the software development lifecycle. Unit testing involves testing individual components or units of code, such as functions or methods, in isolation from the rest of the application. Developers write these tests to ensure that each unit performs as expected, catching bugs early in the development process. Functional testing, on the other hand, evaluates the overall behaviour and functionality of the application. It tests the system as a whole to ensure it meets specified requirements and works correctly from the end-user's perspective. Functional tests involve verifying that various features, interactions and user scenarios function as intended. Key Differences: Unit Testing vs. Functional Testing Feature Unit Testing Functional Testing Focus Individual units of code (functions, classes) Overall application functionality Level of Isolation Isolated from other parts of the system Tests interactions between different components Tester Typically developers Testers or users (black-box testing) Test Case Design Based on code logic and edge cases Based on user stories and requirements Execution Speed Fast and automated Slower and may require manual interaction Defect Detection Catches bugs early in development Identifies issues with overall user experience Example Testing a function that calculates product discount Testing the entire shopping cart checkout process Type of Testing White-box testing (internal code structure is known) Black-box testing (internal code structure is unknown) Scope : Unit Testing : Focuses on individual components or units of code such as functions, methods or classes. Functional Testing : Evaluates the overall behaviour and functionality of the entire application or a major part of it. Objective : Unit Testing : Aims to ensure that each unit of the software performs as expected in isolation. Functional Testing : Seeks to validate that the application functions correctly as a whole and meets the specified requirements. Execution : Unit Testing : Typically performed by developers during the coding phase. Tests are automated and run frequently. Functional Testing : Conducted by QA testers or dedicated testing teams. It can be automated but often involves manual testing as well. Techniques Used : Unit Testing : Uses white-box testing techniques where the internal logic of the code is known and tested. Functional Testing : Employs black-box testing techniques , focusing on input and output without regard to internal code structure. Dependencies : Unit Testing : Tests units in isolation, often using mocks and stubs to simulate interactions with other components. Functional Testing : Tests the application as a whole, including interactions between different components and systems. Timing : Unit Testing : Conducted early in the development process, often integrated into continuous integration/continuous deployment (CI/CD) pipelines . Functional Testing : Typically performed after unit testing, during the later stages of development, such as system testing and acceptance testing. Bug Detection : Unit Testing : Catches bugs at an early stage, making it easier and cheaper to fix them. Functional Testing : Identifies issues related to user workflows, integration points, and overall system behaviour. 💡 Catch all the regressions beforehand, even before they hit production and cause problems to the end-users, eventually asking for a rollback. Check it here. Understanding these key differences in unit testing vs. functional testing helps organisations implement a strong testing strategy, ensuring both the correctness of individual components and the functionality of the entire system. Conclusion Unit testing focuses on verifying individual components in isolation, ensuring each part works correctly. Functional testing, on the other hand, evaluates the entire application to confirm it meets the specified requirements and functions properly as a whole. HyperTest , an integration tool that does not requires all your services to be kept up and live, excels in both unit testing and functional testing, providing a platform that integrates freely with CI/CD tools. For unit testing, HyperTest offers advanced mocking capabilities, enabling precise testing of individual services. In functional testing, HyperTest automates end-to-end test scenarios, ensuring the application behaves as expected in real-world conditions. For more on how HyperTest can help with your unit testing and functional testing needs, visit the website now ! Related to Integration Testing Frequently Asked Questions 1. Who typically performs unit testing? - Unit testing is typically done by developers themselves during the development process. - They write test cases to ensure individual code units, like functions or classes, function as expected. 2. Is selenium a front-end or backend? - Functional testing is usually carried out by testers after the development phase is complete. - Their focus is to verify if the entire system meets its designed functionalities and delivers the intended experience to the end-user. 3. What is the main difference between unit testing and functional testing? Unit testing isolates and tests individual code units, while functional testing evaluates the functionality of the entire system from a user's perspective. For your next read Dive deeper with these related posts! 11 Min. 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Explore essential code coverage techniques and best practices to boost software quality. Learn about statement, branch, path, loop, function, and condition coverage. 30 July 2024 07 Min. Read Code Coverage Techniques: Best Practices for Developers WhatsApp LinkedIn X (Twitter) Copy link Checklist for best practices Developers often struggle to identify untested portions of your codebase, which can lead to potential bugs and unexpected behavior in production. You might find that traditional testing methods miss critical paths and edge cases, which leads to poor quality of the software applications. Code coverage techniques offer a systematic approach to this problem. It measures how much of the source code is tested and proved to enhance testing effectiveness. In this blog, we will discuss the code coverage techniques and best practices that will help developers achieve higher coverage. So, let us get started. Understanding Code Coverage It's an easy yet crucial concept that measures how thoroughly your tests evaluate your code. In simple terms, it tells us the extent to which the application's code is tested when you run a test suite. You can take it as a way to ensure that every nook and cranny of your code is checked for issues. It's a type of White Box Testing typically carried out by developers during Unit Testing. When you run code coverage scripts, they generate a report showing how much of your application code has been executed. At the end of development, every client expects a quality software product, and the developer team is responsible for delivering this. Quality that is required to be checked includes the product's performance, functionality, behavior, correctness, reliability, effectiveness, security, and maintainability. The code coverage metric helps assess these performance and quality aspects of any software. The formula for calculating code coverage is: Code Coverage = (Number of lines of code executed / Total number of lines of code in a system component) * 100 Why Code Coverage? Here are some reasons why performing code coverage is important for you: Ensures Adequate Testing: It helps you determine if there are enough tests in the unit test suite. If the coverage is lacking, you know more tests need to be added to ensure comprehensive testing. Maintains Testing Standards : As you develop software applications, new features and fixes are added to the codebase. Whenever you make changes, the test code should also be updated. Code coverage helps you confirm that the testing standards set at the beginning of the software project are maintained throughout the Software Development Life Cycle. Reduces Bugs: High coverage percentages indicate fewer chances of unidentified bugs in the software application. When you perform testing in production, it's recommended to set a minimum coverage rate that should be achieved. This lowers the chance of bugs being detected after the software development is complete. Constantly fixing bugs can take you away from working on new features and improvements. That's where HyperTest comes in. It helps by catching logical and functional errors early, so you can spend more time building new features instead of dealing with endless bug fixes. HyperTest is designed to tackle this problem. It automatically discovers and tests realistic user scenarios from production, including those tricky edge cases, to ensure that every critical user action is covered. By detecting a wide range of issues, from fatal crashes to contract failures and data errors, HyperTest gives you confidence that your integration is solid and reliable. Supports Scalability : It also ensures that as you scale and modify the software, the quality of the code remains high, allowing for easy introduction of new changes. Now let us move forward to understand about the code coverage techniques that you can leverage to measure the line of code: Code Coverage Techniques Code coverage techniques help ensure that software applications are robust and bug-free. Here are some of the common code coverage techniques that you can use to enhance the test process. Statement Coverage Statement Coverage, also known as Block Coverage, is a code coverage technique that helps ensure that every executable statement in your code has been run at least once. With this, you make sure that all lines and statements in your source code are covered. To achieve this, you might need to test different input values to cover all the various conditions, especially since your code can include different elements like operators, loops, functions, and exception handlers. You can calculate Statement Coverage with this formula: Statement Coverage Percentage = (Number of statements executed) / (Total Number of statements) * 100 Pros: It’s simple and easy to understand. It covers missing statements, unused branches, unused statements and dead code. Cons: It doesn’t ensure that all possible paths or conditions are tested. Branch Coverage It is also known as Decision coverage. This code coverage technique ensures that every branch in your conditional structures is executed at least once. It means that it checks that every possible outcome of your conditions is tested, giving you a clearer picture of how your code behaves under different scenarios. Since Branch Coverage measures execution paths, it offers more depth than Statement Coverage. In fact, achieving 100% Branch Coverage means you’ve also achieved 100% Statement Coverage. To calculate Decision Coverage, use this formula: Decision Coverage Percentage = (Number of decision/branch outcomes executed) / (Total number of decision outcomes in the source code) * 100 Pros: It provides more thorough testing compared to Statement Coverage. Cons: It can be more complex to implement, especially if your code has many branches. Loop Coverage Loop Coverage focuses specifically on testing loops within your code. It makes sure you are testing the loops in different scenarios: with zero iterations, one iteration, and multiple iterations. This helps to ensure that your loops are handling all possible scenarios properly. You can calculate Loop Coverage using this formula: Loop Coverage=Total Number of Loop Scenarios/Number of Executed Loop Scenarios×100% Pros: It provides robust testing of loops, which are often a source of bugs. Cons: It can be redundant if not managed carefully, as some loop scenarios might already be covered by other testing techniques. Path Coverage The main aim of path coverage is to test all the potential paths through which a section of your code is executed. This code coverage technique gives you a comprehensive view by considering different ways the code can run, including various loops and conditional branches. It ensures that you can test all possible routes the code might take. You can calculate Path Coverage using this formula: Path Coverage=Total Number of Possible Paths / Number of Executed Paths×100% Pros: It offers the most thorough testing by covering all possible execution paths. Cons: It can become extremely complex and impractical for large codebases due to the sheer number of possible paths. Function coverage This code coverage technique focuses on making sure that every function in your source code is executed during testing. If you want to get a through test, you have to test each function with different values. Since your code might have multiple functions that may or may not be called depending on the input values, Function Coverage ensures that every function is included in the test process. You can calculate Function Coverage using this formula: Function Coverage Percentage = (Number of functions called) / (Total number of functions) * 100 Pros: It’s easy to measure and implement. Cons: It doesn’t ensure that the internal logic of each function is tested in detail. Condition Coverage Loop coverage or expression coverage mainly focuses on testing and evaluating the variables or sub-expressions within your conditional statements. This code coverage technique is effective in ensuring that tests cover both possible values of the conditions—true and false. When it is done , you can have better insight into the control flow of your code compared to Decision Coverage. This approach specifically looks at expressions with logical operands. You can calculate Condition Coverage using this formula: Condition Coverage Percentage = (Number of Executed Operands / Total Number of Operands) * 100 Pros: It helps identify potential issues in complex conditions. Cons: It can lead to a large number of test cases if your code has many conditions. Code Coverage Best Practices Improving your code coverage is key to overcoming its challenges. To get the most out of your testing, you need to adopt a strategic approach and follow some best practices. Here’s how you can enhance your code coverage: Set Realistic Targets: Focus on high-impact areas like critical logic and security components. Aiming for 100% coverage might be impractical, so prioritize where it matters most. Write Testable Code : Make your code easy to test by: Breaking it into modular components. Using small, self-contained functions. Applying SOLID principles and dependency injection. Prioritize Test Cases: Not all test cases are created equal. Prioritize them based on their impact on coverage and their ability to uncover bugs: Critical functionalities and edge cases. Boundary values. Complex code segments like nested loops. Use Mocks and Stubs: These tools help isolate components and test various scenarios by mimicking behavior and managing dependencies. HyperTest makes managing external components easier for you by mocking them and automatically updating these mocks whenever the behavior of dependencies changes. Continuously Improve: Regularly review and update coverage reports to address gaps and keep up with code changes. Conclusion When it comes to delivering robust and reliable software, understanding code coverage techniques is key for you as a developer. By setting realistic targets and writing testable code, you can make sure that your tests are both efficient and effective. Keep in mind that consistently improving and periodically reviewing coverage reports will help your tests adapt alongside your codebase. Implementing these methods will result in increased code coverage, ultimately resulting in improved software quality and performance. Related to Integration Testing Frequently Asked Questions 1. What is code coverage? Code coverage measures how much of your application's source code is executed during testing. It helps determine if all parts of your code are tested to identify untested portions and potential issues. 2. What is the best software testing tool? Code coverage ensures adequate testing, maintains testing standards throughout development, reduces the likelihood of bugs, and supports scalability as the software evolves. 4. How can I improve my code coverage? Set realistic targets, write testable code by making it modular, prioritize impactful test cases, use mocks and stubs to isolate components, and continuously review and update coverage reports to address gaps and adapt to changes. For your next read Dive deeper with these related posts! 07 Min. 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Considering manual vs. automation testing? Read our blog for a comprehensive comparison and make informed decisions for robust software testing 7 December 2023 12 Min. Read Manual Testing vs Automation Testing : Key Differences WhatsApp LinkedIn X (Twitter) Copy link Get the Comparison Sheet Let’s start this hot discussion by opening with the most debated and burning question, Is manual testing still relevant in the era where AI has taken over, what’s the future of manual testing and the manual testers thereof? What’s the need of manual testing in the era of AI and automation all around? It is an undeniable fact that with the rise in automation and AI, manual testing has definitely taken a back seat. It is all over the internet that manual testing is dying, manual testers are not required anymore. But with what argument? Simply because automation and AI is seeing all the limelight these days, it is not true in all senses that it can completely take over the job of a manual tester or completely eliminate manual testing. Let’s break it down and understand why have this opposing opinion despite of witnessing all the trends: 👉 When a product or software is newly introduced to the market, it's in its early stages of real-world use. At this point, the focus is often on understanding how users interact with the product, identifying unforeseen bugs or issues, and rapidly iterating based on user feedback. Let’s understand this with the help of an example: Consider a new social media app that has just been released. The development team has assumptions about how users will interact with the app, but once it's in the hands of real users, new and unexpected usage patterns emerge. For instance, users might use the chat feature in a way that wasn't anticipated, leading to performance issues or bugs. In this case, manual testers can quickly adapt their testing strategies to explore these unforeseen use-cases. They can simulate the behavior of real users, providing immediate insights into how the app performs under these new conditions. On the other hand, if the team had invested heavily in automation testing from the start, they would need to spend additional time and resources to constantly update their test scripts to cover these new scenarios, which could be a less efficient use of resources at this early stage. 👉 New software features often bring uncertainties that manual testing can effectively address. Manual testers engage in exploratory testing, which is unstructured and innovative, allowing them to mimic real user behaviors that automated tests may miss. This approach is vital in agile environments for quickly iterating new features. Automated testing setup for these features can be resource-intensive, especially when features frequently change in early development stages. However, once a feature is stable after thorough manual testing, transitioning to automated testing is beneficial for long-term reliability and integration with other software components. A 2019 report by the Capgemini Research Institute found that while automation can reduce the cost of testing over time, the initial setup and maintenance could be resource-intensive, especially for new or frequently changing features. Let’s understand this with the help of an example: Consider a software team adding a new payment integration feature to their e-commerce platform. This feature is complex, involving multiple steps and external payment service interactions. Initially, manual testers explore this feature, mimicking various user behaviors and payment scenarios. They quickly identify issues like unexpected timeouts or user interface glitches that weren't anticipated. In this phase, the team can rapidly iterate on the feature based on the manual testing feedback, something that would be slower with automation due to the need for script updates. Once the feature is stable and the user interaction patterns are well understood, it's then automated for regression testing , ensuring that future updates do not break this feature. While automation is integral to modern software testing strategies, the significance of manual testing, particularly for new features and new products, cannot be overstated. Its flexibility, cost-effectiveness, and capacity for immediate feedback make it ideal in the early stages of feature and product development. Now that we’ve established ground on why manual testing is still needed and can never be eliminated from the software testing phase anytime soon, let’s dive deep into the foundational concepts of both the manual and automation testing and understand both of them a little better. Manual Testing vs Automation Testing Manual Testing and Automation Testing are two fundamental approaches in the software testing domain, each with its own set of advantages, challenges, and best use cases. Manual Testing It refers to the process of manually executing test cases without the use of any automated tools. It is a hands-on process where a tester assumes the role of an end-user and tests the software to identify any unexpected behavior or bugs. Manual testing is best suited for exploratory testing, usability testing, and ad-hoc testing where the tester's experience and intuition are critical. Automation Testing It involves using automated tools to execute pre-scripted tests on the software application before it is released into production. This type of testing is used to execute repetitive tasks and regression tests which are time-consuming and difficult to perform manually. Automation testing is ideal for large scale test cases, repetitive tasks, and for testing scenarios that are too tedious for manual testing. A study by the QA Vector Analytics in 2020 suggested that while over 80% of organizations see automation as a key part of their testing strategy, the majority still rely on manual testing for new features to ensure quality before moving to automation. Here is a detailed comparison table highlighting the key differences between Manual Testing vs Automation Testing: Aspect Manual Testing Automation Testing Nature Human-driven, requires physical execution by testers. Tool-driven, tests are executed automatically by software. Initial Cost Lower, as it requires minimal tooling. Higher, due to the cost of automation tools and script development. Execution Speed Slower, as it depends on human speed. Faster, as computers execute tests rapidly. Accuracy Prone to human error. Highly accurate, minimal risk of errors. Complexity of Setup Simple, as it often requires no additional setup. Complex, requires setting up and maintaining test scripts. Flexibility High, easy to adapt to changes and new requirements. Low, requires updates to scripts for changes in the application. Testing Types Best Suited Exploratory, Usability, Ad-Hoc. Regression, Load, Performance. Feedback Qualitative, provides insight into user experience. Quantitative, focuses on specific, measurable outcomes. Scalability Limited scalability due to human resource constraints. Highly scalable, can run multiple tests simultaneously. Suitability for Complex Applications Suitable for applications with frequent changes. More suitable for stable applications with fewer changes. Maintenance Low, requires minimal updates. High, scripts require regular updates. How does Manual Testing work? Manual Testing is a fundamental process in software quality assurance where a tester manually operates a software application to detect any defects or issues that might affect its functionality, usability, or performance. Understanding Requirements : Testers begin by understanding the software requirements, functionalities, and objectives. This involves studying requirement documents, user stories, or design specifications. Developing Test Cases : Based on the requirements, testers write test cases that outline the steps to be taken, input data, and the expected outcomes. These test cases are designed to cover all functionalities of the application. Setting Up Test Environment : Before starting the tests, the required environment is set up. This could include configuring hardware and software, setting up databases, etc. Executing Test Cases : Testers manually execute the test cases. They interact with the software, input data, and observe the outcomes, comparing them with the expected results noted in the test cases. Recording Results : The outcomes of the test cases are recorded. Any discrepancies between the expected and actual results are noted as defects or bugs. Reporting Bugs : Detected bugs are reported in a bug tracking system with details like severity, steps to reproduce, and screenshots if necessary. Retesting and Regression Testing : After the bugs are fixed, testers retest the functionalities to ensure the fixes work as expected. They also perform regression testing to check if the new changes have not adversely affected the existing functionalities. Final Testing and Closure : Once all major bugs are fixed and the software meets the required quality standards, the final round of testing is conducted before the software is released. Case Study: Manual Testing at WhatsApp WhatsApp, a globally renowned messaging app, frequently updates its platform to introduce new features and enhance user experience. Given its massive user base and the critical nature of its service, ensuring the highest quality and reliability of new features is paramount. Challenge : In one of its updates, WhatsApp planned to roll out a new encryption feature to enhance user privacy. The challenge was to ensure that this feature worked seamlessly across different devices, operating systems, and network conditions without compromising the app's performance or user experience. Approach : WhatsApp's testing team employed manual testing for this critical update. The process involved: Test Planning : The team developed a comprehensive test plan focusing on the encryption feature, covering various user scenarios and interactions. Test Case Creation : Detailed test cases were designed to assess the functionality of the encryption feature, including scenarios like initiating conversations, group chats, media sharing, and message backup and restoration. Cross-Platform Testing : Manual testers executed these test cases across a wide range of devices and operating systems to ensure compatibility and consistent user experience. Usability Testing : Special emphasis was placed on usability testing to ensure that the encryption feature did not negatively impact the app's user interface and ease of use. Performance Testing : Manual testing also included assessing the app's performance in different network conditions, ensuring that encryption did not lead to significant delays or resource consumption. Outcome : The manual testing approach allowed WhatsApp to meticulously evaluate the new encryption feature in real-world scenarios, ensuring it met their high standards of quality and reliability. The successful rollout of the feature was well-received by users and industry experts, showcasing the effectiveness of thorough manual testing in a complex, user-centric application environment. How does Automation Testing work? Automation Testing is a process in software testing where automated tools are used to execute predefined test scripts on a software application. This approach is particularly effective for repetitive tasks and regression testing, where the same set of tests needs to be run multiple times over the software's lifecycle. Identifying Test Requirements : Just like manual testing, automation testing begins with understanding the software's functionality and requirements. The scope for automation is identified, focusing on areas that benefit most from automated testing like repetitive tasks, data-driven tests, and regression tests. Selecting the Right Tools : Choosing appropriate automation tools is crucial. The selection depends on the software type, technology stack, budget, and the skill set of the testing team. Designing Test Scripts : Testers or automation engineers develop test scripts using the chosen tool. These scripts are designed to automatically execute predefined actions on the software application. Setting Up Test Environment : Automation testing requires a stable and consistent environment. This includes setting up servers, databases, and any other required software. Executing Test Scripts : Automated test scripts are executed, which can be scheduled or triggered as needed. These scripts interact with the application, input data, and then compare the actual outcomes with the expected results. Analyzing Results : Automated tests generate detailed test reports. Testers analyze these results to identify any failures or issues. Maintenance : Test scripts require regular updates to keep up with changes in the software application. This maintenance is critical for the effectiveness of automated testing. Continuous Integration : Automation testing often integrates into continuous integration/continuous deployment (CI/CD) pipelines , enabling continuous testing and delivery. Case Study: Automation Testing at Netflix Netflix, a leader in the streaming service industry, operates on a massive scale with millions of users worldwide. To maintain its high standard of service and continuously enhance user experience, Netflix frequently updates its platform and adds new features. Challenge : The primary challenge for Netflix was ensuring the quality and performance of its application across different devices and operating systems, particularly when rolling out new features or updates. Given the scale and frequency of these updates, manual testing alone was not feasible. Approach : Netflix turned to automation testing to address this challenge. The process involved: Tool Selection : Netflix selected advanced automation tools compatible with its technology stack, capable of handling complex, large-scale testing scenarios. Script Development : Test scripts were developed to cover a wide range of functionalities, including user login, content streaming, user interface interactions, and cross-device compatibility. Continuous Integration and Deployment : These test scripts were integrated into Netflix's CI/CD pipeline . This integration allowed for automated testing to be performed with each code commit, ensuring immediate feedback and rapid issue resolution. Performance and Load Testing : Automation testing at Netflix also included performance and load testing. Scripts were designed to simulate various user behaviors and high-traffic scenarios to ensure the platform's stability and performance under stress. Regular Updates and Maintenance : Given the dynamic nature of the Netflix platform, the test scripts were regularly updated to adapt to new features and changes in the application. Outcome : The adoption of automation testing enabled Netflix to maintain a high quality of service while rapidly scaling and updating its platform. The automated tests provided quick feedback on new releases, significantly reducing the time to market for new features and updates. This approach also ensured a consistent and reliable user experience across various devices and operating systems. Manual Testing Pros and Cons 1.Pros of Manual Testing: 1.1. Flexibility and Adaptability : Manual testing is inherently flexible. Testers can quickly adapt their testing strategies based on their observations and insights. For example, while testing a mobile application, a tester might notice a usability issue that wasn't part of the original test plan and immediately investigate it further. 1.2. Intuitive Evaluation : Human testers bring an element of intuition and understanding of user behavior that automated tests cannot replicate. This is particularly important in usability and user experience testing. For instance, a tester can judge the ease of use and aesthetics of a web interface, which automated tools might overlook. 1.3.Cost-Effective for Small Projects : For small projects or in cases where the software undergoes frequent changes, manual testing can be more cost-effective as it doesn’t require a significant investment in automated testing tools or script development. 1.4. No Need for Complex Test Scripts : Manual testing doesn’t require the setup and maintenance of test scripts, making it easier to start testing early in the development process. It's especially useful during the initial development stages where the software is still evolving. 1.5. Better for Exploratory Testing : Manual testing is ideal for exploratory testing where the tester actively explores the software to identify defects and assess its capabilities without predefined test cases. This can lead to the discovery of critical bugs that were not anticipated. 2.Cons of Manual Testing: 2.1. Time-Consuming and Less Efficient : Manual testing can be labor-intensive and slower compared to automated testing, especially for large-scale and repetitive tasks. For example, regression testing a complex application manually can take a significant amount of time. 2.2. Prone to Human Error : Since manual testing relies on human effort, it's subject to human errors such as oversight or fatigue, particularly in repetitive and detailed-oriented tasks. 2.3. Limited in Scope and Scalability : There's a limit to the amount and complexity of testing that can be achieved manually. In cases like load testing where you need to simulate thousands of users, manual testing is not practical. 2.4. Not Suitable for Large Volume Testing : Testing scenarios that require a large volume of data input, like stress testing an application, are not feasible with manual testing due to the limitations in speed and accuracy. 2.5. Difficult to Replicate : Manual test cases can be subjective and may vary slightly with each execution, making it hard to replicate the exact testing scenario. This inconsistency can be a drawback when trying to reproduce bugs. Automated Testing Pros and Cons 1. Pros of Automation Testing: 1.1. Increased Efficiency : Automation significantly speeds up the testing process, especially for large-scale and repetitive tasks. For example, regression testing can be executed quickly and frequently, ensuring that new changes haven’t adversely affected existing functionalities. 1.2. Consistency and Accuracy : Automated tests eliminate the variability and errors that come with human testing. Tests can be run identically every time, ensuring consistency and accuracy in results. 1.3. Scalability : Automation allows for testing a wide range of scenarios simultaneously, which is particularly useful in load and performance testing. For instance, simulating thousands of users interacting with a web application to test its performance under stress. 1.4. Cost-Effective in the Long Run : Although the initial investment might be high, automated testing can be more cost-effective over time, especially for products with a long lifecycle or for projects where the same tests need to be run repeatedly. 1.5. Better Coverage : Automation testing can cover a vast number of test cases and complex scenarios, which might be impractical or impossible to execute manually in a reasonable timeframe. 2. Cons of Automation Testing: 2.1. High Initial Investment : Setting up automation testing requires a significant initial investment in tools and script development, which can be a barrier for smaller projects or startups. 2.2. Maintenance of Test Scripts : Automated test scripts require regular updates to keep pace with changes in the application. This maintenance can be time-consuming and requires skilled resources. Learn how this unique record and replay approach lets you take away this pain of maintaining test scripts. 2.3. Limited to Predefined Scenarios : Automation testing is limited to scenarios that are known and have been scripted. It is not suitable for exploratory testing where the goal is to discover unknown issues. 2.4. Lack of Intuitive Feedback : Automated tests lack the human element; they cannot judge the usability or aesthetics of an application, which are crucial aspects of user experience. 2.5. Skillset Requirement : Developing and maintaining automated tests require a specific skill set. Teams need to have or develop expertise in scripting and using automation tools effectively. Don’t forget to download this quick comparison cheat sheet between manual and automation testing. Automate Everything With HyperTest Once your software is stable enough to move to automation testing, be sure to invest in tools that covers end-to-end test case scenarios, leaving no edge cases to be left untested. HyperTest is one such modern no-code tool that not only gives up to 90% test coverage but also reduces your testing effort by up to 85%. No-code tool to test integrations for services, apps or APIs Test REST, GraphQL, SOAP, gRPC APIs in seconds Build a regression test suite from real-world scenarios Detect issues early in SDLC, prevent rollbacks We helped agile teams like Nykaa, Porter, Urban Company etc. achieve 2X release velocity & robust test coverage of >85% without any manual efforts. Give HyperTest a try for free today and see the difference. Frequently Asked Questions 1. Which is better manual testing or automation testing? The choice between manual testing and automation testing depends on project requirements. Manual testing offers flexibility and is suitable for exploratory and ad-hoc testing. Automation testing excels in repetitive tasks, providing efficiency and faster feedback. A balanced approach, combining both, is often ideal for comprehensive software testing. 2. What are the disadvantages of manual testing? Manual testing can be time-consuming, prone to human error, and challenging to scale. The repetitive nature of manual tests makes it monotonous, potentially leading to oversight. Additionally, it lacks the efficiency and speed offered by automated testing, hindering rapid development cycles and comprehensive test coverage. 3. Is automation testing better than manual testing? Automation testing offers efficiency, speed, and repeatability, making it advantageous for repetitive tasks and large-scale testing. However, manual testing excels in exploratory testing and assessing user experience. The choice depends on project needs, with a balanced approach often yielding the most effective results, combining the strengths of both automation and manual testing. For your next read Dive deeper with these related posts! 08 Min. Read What is API Test Automation?: Tools and Best Practices Learn More 07 Min. Read What is API Testing? Types and Best Practices Learn More 09 Min. Read API Testing vs UI Testing: Why API is better than UI? Learn More

All Events Dive deep into our expanding collection of practical tips on achieving bug-free development: Available live, and through pre-recorded sessions. Events Past Event Type Online Event Category API Testing Best Practices Contract Testing E2E Testing GenAI for Testing Mocking Unit Testing Clear all Mocking Mock APIs Message Queues and Databases in One Place 29 January 2025 09.00 AM EST, 7:30 PM IST Watch Now Best Practices Implementing TDD: Organizational Struggles & Fixes 18 December 2024 09.00 AM EST, 7:30 PM IST Watch Now Best Practices Get to 90%+ coverage in less than a day without writing tests 28 November 2024 09.00 AM EST, 7:30 PM IST Watch Now Best Practices Build E2E Integration Tests Without Managing Test Environments or Test Data 13 November 2024 09.00 AM EST, 7:30 PM IST Watch Now Unit Testing No more Writing Mocks: The Future of Unit & Integration Testing 6 October 2024 10.00 AM EDT, 7:30 PM IST Watch Now Best Practices Ways to tackle Engineering Problems of High Growth Teams 30 May 2024 10.00 AM EDT, 7:30 PM IST Watch Now Best Practices Zero to Million Users: How Fyers built and scaled one of the best trading app 20 March 2024 09.00 AM EST, 7:30 PM IST Watch Now Contract Testing Masterclass on Contract Testing: The Key to Robust Applications 28 February 2024 09.00 AM EST, 7:30 PM IST Watch Now API Testing Why Clever Testers Prioritize API Testing Over UI Automation 8 January 2024 09.00 AM EST, 7:30 PM IST Watch Now E2E Testing How to do End-to-End testing without preparing test data? 30 November 2023 10.00 AM EDT, 7:30 PM IST Watch Now GenAI for Testing What no one will tell you about using GenAI for Testing 25 October 2023 10.00 AM EDT, 7:30 PM IST Watch Now Get State of API Testing Report: Regression Trends 2023 Download Now

Postman vs HyperTest: Which API Testing Tool is Better? Postman vs HyperTest Welcome to our comprehensive comparison of Postman and HyperTest. Whether you are looking for the best API testing tool for your needs, we've got you covered. What is Postman? Postman is a popular API development and testing tool that simplifies the process of creating, sharing, testing, and documenting APIs. It offers a user-friendly interface and a range of features that make it a go-to tool for many developers. Pros of Postman User-Friendly Interface: Postman's intuitive interface makes it easy for developers of all skill levels to create and test APIs. Comprehensive Documentation: Postman provides excellent documentation capabilities, allowing developers to easily share API details with their teams. Collaboration Features: Postman enables team collaboration through shared collections and environments. Automation Capabilities: Postman supports automated testing through its scripting capabilities using JavaScript. Integration with CI/CD: Postman integrates well with continuous integration and continuous deployment (CI/CD) pipelines. Cons of Postman Resource Intensive: Postman can be resource-heavy, especially when dealing with large collections. Learning Curve for Advanced Features: While the basic features are easy to use, mastering advanced features may require some time and effort. What is HyperTest? HyperTest is an innovative automated testing platform designed for high-speed, parallel execution of API tests. It aims to optimize testing efficiency and reduce the time required for test execution. Advantages and Disadvantages of HyperTest Pros of HyperTest High-Speed Execution: HyperTest is designed for speed, allowing for rapid execution of API tests in parallel. Scalability: HyperTest can handle large-scale test execution, making it suitable for enterprise-level applications. Automation and Integration: HyperTest integrates seamlessly with CI/CD pipelines and supports automated testing workflows. Comprehensive Reporting: HyperTest provides detailed test reports, helping teams quickly identify and address issues. Ease of Use: Despite its advanced capabilities, HyperTest offers a user-friendly interface. Cons of HyperTest Cost: HyperTest may be more expensive compared to other API testing tools, which could be a consideration for smaller teams or projects. Learning Curve: While the tool is powerful, it may require some time to fully understand and utilize its advanced features. Next Item Previous Item

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