To solve the problem of lengthy and resource-intensive software testing cycles, here are the detailed steps for understanding and implementing parallel testing:

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Parallel testing is a software testing technique where multiple tests or test cases are executed simultaneously on different environments, machines, or configurations. The core idea is to drastically reduce the overall execution time by leveraging concurrent processing. Instead of running tests one after another in a sequential manner, parallel testing runs them at the same time, much like having multiple cashiers open at a busy supermarket versus just one. This approach is invaluable in agile and DevOps environments where continuous integration and continuous delivery CI/CD pipelines demand rapid feedback. It’s a must for large-scale projects or applications with extensive test suites, as it directly impacts time-to-market and the efficiency of the development lifecycle. Think of it as a strategic move to optimize your testing efforts, ensuring faster validation and quicker deployment of high-quality software. It can involve running the same test suite against different browsers, operating systems, or device combinations simultaneously, or running entirely different test suites concurrently on a single build. The objective is to achieve broader test coverage in less time, identifying bugs earlier in the development cycle.

The Core Mechanics: How Parallel Testing Works

Parallel testing, at its heart, is about concurrency and efficiency. It’s not just about speeding things up.

It’s about making smarter use of your computational resources to get the job done faster. Imagine you have a massive stack of books to read. Sequential reading means one book after another.

Parallel reading, if possible, would mean reading several books at once, or perhaps having a team read different books simultaneously.

In software, this translates to executing multiple test scripts or even entire test suites at the same time.

Leveraging Multiple Environments

One of the most common applications of parallel testing is to execute the same test suite across various environments concurrently. What is browser sandboxing

• Browser Compatibility: For web applications, this means running tests on Chrome, Firefox, Safari, and Edge simultaneously. Instead of waiting for one browser’s tests to complete before starting the next, all browser tests run in parallel. This is crucial for ensuring a consistent user experience across different platforms.
• Operating Systems: Similarly, a desktop application might need testing on Windows, macOS, and various Linux distributions. Parallel testing allows these OS-specific tests to run in unison.
• Device Combinations: Mobile applications benefit immensely by running tests on multiple Android and iOS devices, different screen sizes, and various OS versions simultaneously. This provides broad coverage without the agonizing wait.
• Virtual Machines and Containers: Modern testing infrastructures heavily rely on VMs and containers like Docker. Each container or VM can host a distinct test environment, allowing for highly isolated and parallel execution of tests. This ensures that tests don’t interfere with each other and that each test runs in a pristine state.

Distributing the Workload

Beyond different environments, parallel testing also involves distributing segments of a large test suite.

• Test Suite Partitioning: A monolithic test suite can be broken down into smaller, independent chunks. These chunks are then executed in parallel. For instance, if you have 1000 test cases, you might split them into 10 groups of 100, and run all 10 groups simultaneously on 10 different test agents.
• Cloud-Based Execution: Cloud platforms like AWS, Azure, Google Cloud provide scalable infrastructure on demand. This allows teams to spin up hundreds or even thousands of test machines in minutes, enabling massive parallel execution. Data shows that companies leveraging cloud-based parallel testing can reduce their test execution time by up to 90%. For example, a test suite that takes 10 hours sequentially might finish in less than an hour when run in parallel on sufficient cloud resources.
• Test Orchestration Tools: Tools like Selenium Grid, Jenkins, CircleCI, GitLab CI/CD, and specialized testing platforms e.g., Sauce Labs, BrowserStack are designed to orchestrate and manage parallel test execution. They handle the distribution of tests, managing test agents, collecting results, and reporting. Selenium Grid, for instance, allows you to route your tests to different remote machines running various browser and OS combinations.

The Underlying Technology

The magic behind parallel testing often involves:

• Multi-threading/Multi-processing: At a fundamental level, parallel tests utilize multiple threads or processes on a single machine or distributed across multiple machines. Each thread/process handles a specific test or set of tests.
• Asynchronous Operations: Many modern testing frameworks and tools support asynchronous operations, allowing tests to be initiated without waiting for previous ones to complete.
• Load Balancing: When using distributed test environments, sophisticated load-balancing algorithms ensure that tests are evenly distributed across available resources, preventing bottlenecks and maximizing throughput.
• Test Data Management: A critical aspect is ensuring that parallel tests have independent and consistent test data. Shared data can lead to test flakiness and unreliable results. Strategies include creating unique data for each test run or resetting data before each test.

It’s a foundational element of modern CI/CD pipelines, allowing teams to maintain a fast pace of innovation while upholding quality standards.

Why Parallel Testing is a Game-Changer for Modern Development

It’s a strategic necessity that fundamentally transforms how quickly and effectively software can be delivered.

It’s about breaking free from the bottlenecks of linear processes and embracing concurrency. How to perform ios ui test automation

Drastically Reduced Execution Time

This is perhaps the most immediate and impactful benefit.

Consider a large enterprise application with a test suite of 5,000 end-to-end tests, each taking, on average, 30 seconds to run.

• Sequential Execution: 5,000 tests * 30 seconds/test = 150,000 seconds = 2,500 minutes = ~41.6 hours. This means a full regression suite takes almost two full days to run, effectively blocking rapid deployment.
• Parallel Execution: If you can run 100 tests in parallel, the execution time reduces to 5,000 tests / 100 parallel runs * 30 seconds/test = 50 * 30 seconds = 1,500 seconds = 25 minutes. This is a staggering 99% reduction in execution time.
  This speed-up means that:
• Faster Feedback Loops: Developers get immediate feedback on code changes, allowing them to identify and fix bugs much earlier in the development cycle, when they are significantly cheaper to fix. A Google study found that the cost of fixing a bug increases exponentially the later it’s found in the SDLC.
• More Frequent Releases: With testing no longer a bottleneck, teams can release new features and bug fixes much more frequently, aligning with DevOps principles of continuous delivery. Industry leaders often perform hundreds or even thousands of deployments per day, a feat impossible without extensive parallel testing.

Enhanced Test Coverage

While speed is a major driver, parallel testing also allows for broader and deeper test coverage.

• Cross-Browser/Cross-Platform Validation: As mentioned earlier, parallel testing enables simultaneous execution across multiple browsers, operating systems, and device configurations. This ensures that the application behaves consistently regardless of the user’s environment. Without parallel testing, achieving comprehensive coverage across all these permutations would be prohibitively time-consuming.
• Early Detection of Issues: By running a wide array of tests frequently, issues related to specific environments or integrations can be caught earlier. This proactively addresses potential customer pain points before they escalate.
• Scalability for Growth: As applications grow in complexity and user base, the number of test cases inevitably increases. Parallel testing provides the scalability needed to manage these ever-expanding test suites without sacrificing execution speed.

Optimized Resource Utilization

Parallel testing makes smart use of your infrastructure.

• Maximized Hardware Investment: Instead of having expensive servers sit idle while tests run sequentially, parallel testing keeps them busy, maximizing the return on your hardware or cloud infrastructure investment.
• Cost-Effectiveness with Cloud: Cloud-based parallel testing leverages the elasticity of cloud resources. You only pay for the computational power you use during the test execution, scaling up when needed and scaling down afterwards. This “pay-as-you-go” model is often more cost-effective than maintaining a large, always-on on-premise test lab for sequential execution.
• Improved CI/CD Pipeline Efficiency: Parallel testing is a cornerstone of efficient CI/CD pipelines. It allows for the rapid execution of automated tests at every commit, ensuring that only high-quality code moves down the pipeline. This automation reduces manual effort and human error, streamlining the entire development process.

Increased Tester Productivity and Morale

When tests run faster, the impact on human resources is significant. How to run apk online in browser

• Reduced Waiting Times: Testers and developers spend less time waiting for test results, allowing them to focus on more productive tasks like test case creation, analysis, and exploratory testing. This reduces frustration and improves overall team morale.
• Faster Iteration: The quick feedback loop fosters a culture of rapid iteration and continuous improvement. Teams can experiment more freely, knowing that a comprehensive safety net of tests will quickly identify any regressions.
• Better Quality Assurance: With the ability to run more tests more frequently, the overall quality assurance process becomes more robust, leading to a higher quality product delivered to the end-users.

In summary, parallel testing is not just a technical enhancement.

It’s a strategic enabler for agile development, DevOps practices, and ultimately, a faster, more reliable path to delivering exceptional software.

It’s about building an efficient, high-performing testing machine that keeps pace with the demands of modern software delivery.

Key Considerations and Prerequisites for Effective Parallel Testing

While the benefits of parallel testing are immense, it’s not a “set it and forget it” solution.

Proper planning, infrastructure, and test design are crucial for its successful implementation. Protractor alternatives

Overlooking these prerequisites can lead to flaky tests, unreliable results, and ultimately, a waste of resources.

1. Independent and Atomic Test Cases

This is arguably the most critical prerequisite.

For tests to run reliably in parallel, they must be completely independent of each other.

• No Shared State: Tests should not rely on the outcome or state left behind by another test. If Test A modifies data that Test B then uses, running them in parallel can lead to race conditions and unpredictable results.
• Clean Test Data: Each test should operate on its own pristine set of data. This often involves:
  • Data Setup/Teardown: Each test should ideally set up its required data at the beginning and clean it up or revert it at the end. This ensures that the test environment is reset for the next run.
  • Unique Data Generation: For scenarios involving database entries or user profiles, generating unique data for each parallel test instance is vital. Tools and frameworks often provide utilities for this.
  • Immutability: Wherever possible, design your application and tests to work with immutable data or to perform operations that don’t leave lingering side effects.
• Login/Logout Independence: If tests require user authentication, each test or test group should handle its own login and logout processes independently. Don’t rely on a previous test having left a session open.
• Idempotency: Operations within your tests should ideally be idempotent, meaning performing them multiple times has the same effect as performing them once. This helps in recovery and retry scenarios.

2. Robust Test Automation Framework

A solid automation framework is the backbone of parallel testing.

• Framework Support: The chosen automation framework e.g., Selenium, Playwright, Cypress, Appium, Jest must inherently support parallel execution. Many modern frameworks do, often by default or with minimal configuration.
• Reporting and Logging: When tests run in parallel, tracking failures and understanding execution flow becomes more complex. The framework should provide robust logging and reporting capabilities that clearly indicate which test failed, in which environment, and why. Integrations with tools like Allure Reports, ExtentReports, or even simple JUnit XML reports are essential.
• Error Handling and Retries: Parallel environments can sometimes be prone to transient network issues or temporary resource unavailability. The framework should have mechanisms for robust error handling and intelligent test retries to minimize false positives.

3. Scalable Test Infrastructure

You need an infrastructure that can handle the concurrent load. Automated visual testing for netlify sites with percy

• Cloud-Based Solutions Recommended: Services like Sauce Labs, BrowserStack, CrossBrowserTesting provide instant access to hundreds of real devices and browser/OS combinations, specifically designed for parallel execution. They handle the infrastructure setup, maintenance, and scaling. This significantly reduces the overhead for your team. A recent report by TechCrunch highlighted that cloud-based testing adoption has surged by 40% in the last two years, largely due to its scalability for parallel testing.
• On-Premise Grid/Lab: For highly sensitive data or specific hardware requirements, setting up an on-premise Selenium Grid or a custom test lab might be necessary. This requires significant upfront investment in hardware, ongoing maintenance, and expertise to manage. You’ll need sufficient CPU, RAM, and network bandwidth to support all concurrent test runners.
• Containerization Docker: Docker containers are excellent for creating isolated, consistent, and easily scalable test environments. Each test instance can run within its own container, ensuring a clean slate and simplifying dependency management. Orchestration tools like Kubernetes can manage thousands of these containers for massive parallelization.
• Resource Monitoring: Implement monitoring tools to track CPU usage, memory, network I/O, and disk space on your test machines. This helps identify bottlenecks and ensure your infrastructure can sustain the parallel load.

4. Efficient Test Runner Configuration

How you configure your test runner directly impacts parallel efficiency.

• Thread/Process Management: Configure the number of parallel threads or processes based on your available resources. Too many can overwhelm your machines, leading to slower tests due to contention or crashes. Too few won’t maximize the benefits.
• Load Balancing Strategies: If using a test grid, ensure intelligent load balancing distributes tests evenly across available nodes.
• Test Prioritization Optional but Recommended: While all tests run in parallel, you might prioritize critical smoke tests or integration tests to run first, providing quicker feedback on the core functionality. This can be achieved by splitting your test suite into different jobs that run concurrently but in a specific order of importance.

5. Continuous Integration CI Tool Integration

Parallel testing shines brightest when integrated into your CI/CD pipeline.

• Automated Triggers: Tests should be automatically triggered on every code commit or pull request.
• Orchestration: CI tools like Jenkins, GitLab CI, GitHub Actions, CircleCI, or Azure DevOps can orchestrate the parallel execution, distribute tests to agents, collect results, and report status. They become the central hub for your automated testing.
• Result Aggregation: The CI tool should aggregate results from all parallel runs into a single, comprehensive report, making it easy to see overall pass/fail status and drill down into individual test failures.

Ignoring these considerations can turn parallel testing from a performance booster into a source of frustration.

Proper upfront planning and meticulous implementation are key to harnessing its full potential.

Types of Parallel Testing: Beyond the Basics

When we talk about parallel testing, it’s not a monolithic concept. Mobile website compatibility

There are several distinct approaches, each serving a slightly different purpose and offering unique advantages.

Understanding these variations helps you tailor your parallel testing strategy to your specific project needs.

1. Cross-Browser/Cross-Platform Parallel Testing

This is perhaps the most widely recognized form of parallel testing, especially for web and mobile applications.

• Objective: To verify that the application functions correctly and consistently across different browsers, operating systems, and device configurations.
• How it works: The same set of automated tests is executed simultaneously on multiple target environments. For example, a web application’s login flow might be tested on Chrome Windows, Firefox macOS, Edge Windows, and Safari iOS all at the same time.
• Tools:
  • Selenium Grid: Allows you to run tests on different machines with various browsers and OSs. You configure a hub and multiple nodes.
  • Cloud-based platforms: Sauce Labs, BrowserStack, LambdaTest provide extensive cloud infrastructure with thousands of real devices and browser/OS combinations for concurrent execution. These platforms handle the setup and maintenance of the diverse environments.
• Example: An e-commerce site needs to ensure that users can add items to their cart and complete checkout, whether they are using an iPhone, an Android tablet, or a desktop PC with any major browser. Parallel testing runs these critical flows across all permutations concurrently.
• Benefit: Ensures maximum compatibility and a consistent user experience across diverse user environments, significantly reducing the risk of platform-specific bugs reaching production. A report by Statista shows that cross-browser compatibility issues account for approximately 25% of all web application bugs. Parallel testing directly addresses this.

2. Parallel Execution of Different Test Suites/Modules

This approach focuses on accelerating the overall regression cycle by running distinct parts of the test suite concurrently.

• Objective: To reduce the total execution time of a very large test suite by running different sections of it in parallel.
• How it works: A monolithic test suite is broken down into independent modules or functional areas e.g., User Management, Product Catalog, Payment Gateway. Each module’s tests are then run in parallel on separate test agents or machines.
  • CI/CD Tools: Jenkins, GitLab CI, GitHub Actions, Azure DevOps can be configured to trigger multiple independent jobs concurrently. Each job might be responsible for running a specific module’s tests.
  • Test Runners: Frameworks like JUnit with parallel runners, TestNG with parallel execution settings, or pytest-xdist for Python allow for parallel execution of tests within a single framework instance.
• Example: In a banking application, tests for the “Account Balance” module, “Fund Transfer” module, and “Bill Pay” module can all run simultaneously. If each module has 100 tests, running them in parallel means all 300 tests finish around the time the longest single module finishes, rather than 3x that time.
• Benefit: Significantly reduces the total time required for full regression testing, enabling faster validation of new builds and accelerating the CI/CD pipeline. This is crucial for large applications with thousands of test cases.

3. Parallel Execution within a Single Test Framework Test Parallelism

This is about leveraging multi-threading or multi-processing capabilities within a single test runner instance. Selenium grid 4 tutorial

• Objective: To speed up test execution by running individual test methods or classes concurrently on a single machine, or by splitting test files across multiple processes.
• How it works: The test runner itself is configured to execute multiple tests at once, often by creating separate threads or processes for each test.
• Tools/Frameworks:
  • TestNG Java: Supports parallel execution at the method, class, or test XML level.
  • JUnit 5 Java: Provides excellent support for parallel test execution.
  • pytest-xdist Python: A plugin for pytest that enables distributed testing, allowing tests to run in parallel across multiple CPUs or remote hosts.
  • Playwright/Cypress JavaScript: Modern JavaScript frameworks often have built-in parallelization capabilities.
• Example: A UserRegistrationTests.java file might contain 10 test methods. Instead of running testValidRegistration, then testInvalidEmail, then testDuplicateUsername, etc., the framework runs them concurrently.
• Benefit: Provides an easy way to gain significant speed improvements without needing a complex distributed grid setup, particularly effective for CPU-bound tests or when tests are largely independent within a single file or class.

4. Parallel Regression Testing with Baseline

This specific type of parallel testing compares the behavior of a new system version against a baseline version, running tests on both simultaneously.

• Objective: To quickly identify regressions or behavioral changes between a new build and a known stable version.
• How it works: Automated tests are executed on the new version of the software and, in parallel, on the old baseline version. The results from both runs are then compared. Any discrepancies highlight potential regressions.
• Use Case: Often used during major upgrades, system migrations e.g., from an old ERP to a new one, or when integrating a new module. It’s less common for day-to-day CI/CD but highly valuable for specific large-scale validation.
• Benefit: Provides direct, immediate feedback on whether a new release introduces any unintended changes compared to a known good state. This is critical in scenarios where ensuring backward compatibility is paramount, such as financial systems or core infrastructure.

Each type of parallel testing offers distinct advantages and addresses different needs.

A comprehensive testing strategy might even combine several of these approaches to maximize efficiency and coverage.

The key is to choose the right type or combination that aligns with your project’s architecture, testing goals, and available resources.

Setting Up Your Parallel Testing Environment: A Practical Guide

Alright, you’re convinced parallel testing is the way to go. Role of automation testing in ci cd

Now, how do you actually set it up? This isn’t just about flipping a switch.

It requires careful configuration, especially depending on whether you opt for cloud-based services or an on-premise solution. Let’s break down the practical steps.

Option 1: Leveraging Cloud-Based Testing Platforms Recommended

This is by far the easiest and most scalable approach for most teams, especially given the ease of setup and reduced maintenance overhead. Platforms like Sauce Labs, BrowserStack, and LambdaTest are leaders in this space.

1. Choose Your Platform: Research and select a cloud platform that fits your budget, target environments browsers, devices, OSs, and integration needs.
   • Sauce Labs: Known for extensive real device and browser coverage, performance testing, and visual testing. Used by companies like Salesforce and Adobe.
   • BrowserStack: Offers a massive range of real devices and browser environments. Popular for its user-friendly interface and comprehensive features. Used by Microsoft, Spotify.
   • LambdaTest: Provides a scalable cross-browser testing cloud with real devices and emulators/simulators. Often cited for competitive pricing.
2. Integrate with Your Test Framework: These platforms provide SDKs or clear configuration guides for popular test frameworks Selenium, Playwright, Cypress, Appium, Puppeteer, etc..

   • Selenium Example Java/TestNG:

     
     
     import org.openqa.selenium.remote.DesiredCapabilities.
     
     
     import org.openqa.selenium.remote.RemoteWebDriver.
     import java.net.URL.
     import org.testng.annotations.Test.
     import org.testng.annotations.Parameters.
     
     public class SauceLabsParallelTest {
     
     
     
        // Your Sauce Labs Username and Access Key
     
     
        public static final String SAUCE_USERNAME = System.getenv"SAUCE_USERNAME".
     
     
        public static final String SAUCE_ACCESS_KEY = System.getenv"SAUCE_ACCESS_KEY".
     
     
        public static final String SAUCE_URL = "https://" + SAUCE_USERNAME + ":" + SAUCE_ACCESS_KEY + "@ondemand.us-west-1.saucelabs.com/wd/hub".
     
         @Test
     
     
        @Parameters{"browser", "version", "platform"} // Parameters from testng.xml
     
     
        public void runTestString browser, String version, String platform throws Exception {
     
     
            DesiredCapabilities caps = new DesiredCapabilities.
     
     
            caps.setCapability"browserName", browser.
     
     
            caps.setCapability"version", version.
     
     
            caps.setCapability"platform", platform.
     
     
            caps.setCapability"name", "My Parallel Test - " + browser + " " + version.
     
     
     
            RemoteWebDriver driver = new RemoteWebDrivernew URLSAUCE_URL, caps.
     
             // Your test logic here
     
     
            driver.get"https://www.google.com".
     
     
            System.out.println"Page title on " + browser + ": " + driver.getTitle.
     
             driver.quit.
         }
     }
     

     And in your testng.xml for parallel execution: How to test ecommerce website

     
     
     <!DOCTYPE suite SYSTEM "http://testng.org/testng-1.0.dtd">
     
     
     <suite name="SauceLabsSuite" parallel="tests" thread-count="5"> <!-- Run 5 tests in parallel -->
         <test name="ChromeTest">
     
     
            <parameter name="browser" value="Chrome"/>
     
     
            <parameter name="version" value="latest"/>
     
     
            <parameter name="platform" value="Windows 10"/>
             <classes>
     
     
                <class name="SauceLabsParallelTest"/>
             </classes>
         </test>
         <test name="FirefoxTest">
     
     
            <parameter name="browser" value="Firefox"/>
     
     
     
     
     
     
     
     
        <!-- Add more <test> blocks for other browsers/platforms -->
     </suite>
     

3. Configure Parallelism: Within your test runner e.g., testng.xml for TestNG, pytest.ini with pytest-xdist, or framework-specific configurations, specify the degree of parallelism how many concurrent tests you want to run.
4. Integrate with CI/CD: Connect your cloud platform account to your CI/CD pipeline Jenkins, GitLab CI, GitHub Actions, etc.. This usually involves setting environment variables for credentials and calling the test execution command. The CI tool will then trigger your tests on the cloud grid.
5. Monitor and Analyze: Use the cloud platform’s dashboards to monitor test execution, view video recordings of failures, analyze logs, and identify bottlenecks.

Advantages of Cloud:

• Zero Setup/Maintenance: No need to manage physical devices or virtual machines.
• Massive Scalability: Instantly scale up to hundreds or thousands of concurrent tests.
• Wide Coverage: Access to a vast array of real devices, browsers, and OS versions.
• Cost-Effective: Pay-as-you-go model often more efficient than maintaining on-premise infrastructure. A study by TestProject revealed that cloud-based testing reduces infrastructure costs by 30-50% for many organizations.

Option 2: Building an On-Premise Selenium Grid

This approach gives you full control but comes with significant setup and maintenance overhead.

1. Hardware Requirements:

   • Hub Machine: A powerful machine to act as the central hub, coordinating tests. Needs sufficient RAM and CPU.
   • Node Machines: Multiple machines physical or virtual that will run the actual browser instances. Each node needs enough RAM and CPU for the browsers it will host. e.g., 2GB RAM per browser instance is a good starting point.
   • Network: A stable, high-bandwidth local network connection between the hub and nodes.

2. Install Java: Selenium Grid requires Java Runtime Environment JRE to be installed on both hub and node machines.

3. Download Selenium Server JAR: Get the latest selenium-server-standalone.jar from the official Selenium website on all machines. Mobile app testing how to get it right

4. Start the Hub: On your designated hub machine, open a terminal and run:

   
   
   java -jar selenium-server-standalone.jar -role hub -port 4444
   

   This starts the hub, usually on port 4444. You can access its console at http://localhost:4444/grid/console.

5. Configure and Start Nodes: On each node machine, you need to register it with the hub and specify which browsers are available.

   • Install Browsers/Drivers: Install the browsers Chrome, Firefox, etc. and their respective WebDriver executables chromedriver, geckodriver on each node machine. Ensure drivers are in the system’s PATH or specified in the node configuration.
   • Start Node:

     
     
     java -jar selenium-server-standalone.jar -role node -hub http://<hub_ip>:4444/grid/register -browser browserName=chrome,maxInstances=5 -browser browserName=firefox,maxInstances=5 -port 5555
     
     
     Replace `<hub_ip>` with the actual IP address of your hub.
     

maxInstances defines how many concurrent browser sessions of that type the node can handle. Use a unique port for each node.
6. Integrate with Test Framework: Configure your test framework e.g., Selenium WebDriver code to point to your Selenium Grid hub.
“`java
// Example for Selenium Java

DesiredCapabilities caps = DesiredCapabilities.chrome. // Or Firefox, etc.


WebDriver driver = new RemoteWebDrivernew URL"http://<hub_ip>:4444/wd/hub", caps.

7. CI/CD Integration: Your CI tool will trigger your tests, which will then send requests to your on-premise Selenium Grid for parallel execution.

Advantages of On-Premise: Troubleshoot qa issues faster with browserstack and deploy previews

• Full Control: Complete control over the environment, security, and hardware.
• No Internet Dependency for test execution: Tests run on your local network.

Disadvantages of On-Premise:

• High Setup & Maintenance: Requires significant effort to set up, configure, and maintain hardware, software, and browser/driver updates.
• Limited Scalability: Scaling up means adding more physical or virtual machines.
• Diverse Environment Management: Maintaining a wide array of browser/OS versions across multiple machines is complex and time-consuming. Gartner estimates that managing on-premise test infrastructure can cost 2-3 times more than cloud solutions over a 5-year period due to operational overhead.

Whether you choose cloud or on-premise, the goal remains the same: efficient, concurrent test execution to accelerate your software delivery pipeline.

For most modern applications, the cloud offers a compelling balance of scalability, coverage, and ease of use.

Best Practices and Pitfalls to Avoid in Parallel Testing

Implementing parallel testing effectively can drastically improve your testing efficiency, but without adhering to best practices, it can also introduce more problems than it solves.

It’s like having a multi-lane highway: without clear rules and proper maintenance, it can lead to chaos rather than smooth traffic flow. Remote firefox debugging

Best Practices

1. Ensure Test Independence: This is the golden rule. Every test case must be able to run in isolation without affecting or being affected by other tests.

   • Self-Cleaning Tests: Design tests to be self-contained. Each test should set up its own data, perform its actions, and clean up any changes made, ensuring a pristine state for the next test. Use @BeforeMethod/@AfterMethod TestNG/JUnit or similar setup/teardown hooks.
   • Unique Test Data: Avoid shared data. Generate unique user IDs, order numbers, or other relevant data for each test run. This prevents race conditions where tests might try to use or modify the same record concurrently.
   • Stateless Operations: Design your application and tests to be as stateless as possible, or at least manage state carefully so that parallel processes don’t interfere with each other.
   • Focus on Small, Atomic Tests: Smaller, more focused tests are easier to make independent and debug when they fail in a parallel environment.

2. Optimize Test Execution Order Strategic Parallelization: While tests run in parallel, you can still optimize the execution.

   • Prioritize Critical Paths: Run your most critical smoke and sanity tests first. Even if they run in parallel, ensuring these pass immediately provides quick confidence.
   • Group Related Tests: Group tests that interact with similar parts of the system or share common setup steps. This can sometimes improve efficiency by reducing redundant setup, as long as independence is maintained between groups.
   • Flaky Test Identification: Monitor for “flaky” tests – tests that pass sometimes and fail other times without code changes. These are poison to parallel execution. Identify, analyze, and fix them immediately. A study by Facebook found that fixing flaky tests significantly improved developer productivity by reducing wasted cycles.

3. Robust Infrastructure and Scalability:

   • Monitor Resources: Continuously monitor CPU, memory, network I/O, and disk space on your test machines nodes/containers. Overloading machines will lead to slower tests and unpredictable failures.
   • Choose Cloud for Scale: For most organizations, cloud-based testing platforms Sauce Labs, BrowserStack offer the best scalability, maintenance, and breadth of environments. They handle the infrastructure complexities for you.
   • Containerization Docker: For on-premise solutions, containerizing your test environments using Docker ensures consistency and isolation, making it easier to scale horizontally.

4. Effective Reporting and Logging:

   • Centralized Logging: Ensure all test logs are centralized and accessible. When tests run across multiple machines, scattered logs make debugging a nightmare.
   • Clear Pass/Fail Indicators: Reporting should clearly show which tests passed/failed and on which environment.
   • Contextual Information: Logs should include timestamp, test name, thread ID, environment details browser, OS, and relevant debug information for failures screenshots, network logs, page source. Tools like Allure Reports or integrating with an ELK stack Elasticsearch, Logstash, Kibana can provide powerful visualization.

5. Manage Test Data Carefully: Open source spotlight vuetify with john leider

   • Data Isolation: Implement strategies to ensure each parallel test thread/process has its own unique, isolated set of test data. This can involve:
     • Test Data Generators: Generate data on the fly.
     • Database Snapshots/Rollbacks: For complex scenarios, restore a clean database snapshot before each test or transaction.
     • Dedicated Test Users: Create unique user accounts for each parallel test run.
   • Avoid Shared Resources: If tests interact with external systems e.g., third-party APIs, ensure these systems can handle the concurrent load or mock them out where possible to prevent external dependencies from slowing down or destabilizing tests.

Pitfalls to Avoid

1. Ignoring Test Dependencies: The biggest mistake. If tests have implicit dependencies and are run in parallel, you’ll get unpredictable failures and a “flaky” test suite, eroding trust in your automation. This leads to wasted debugging time.
2. Underestimating Infrastructure Needs: Trying to run too many parallel tests on insufficient hardware will lead to resource contention, slow execution, and crashes. It’s often worse than sequential execution. Don’t be stingy with resources. either invest properly in on-premise or leverage the cloud.
3. Poor Test Data Management: Sharing test data without proper isolation can lead to tests interfering with each other’s data, causing false failures or passes. This is a common source of flakiness.
4. Inadequate Reporting: Without clear, centralized, and detailed reporting, debugging parallel test failures becomes a huge manual effort. You won’t know which test failed on which environment, making root cause analysis incredibly difficult.
5. Neglecting Flaky Tests: Running flaky tests in parallel simply amplifies their flakiness. A test that sporadically fails sequentially will fail even more often and unpredictably in parallel, masking real issues and leading to a “boy who cried wolf” scenario where developers ignore test failures.
6. Over-Parallelization: More parallelism isn’t always better. Beyond a certain point, adding more threads or processes can lead to diminishing returns or even negative performance due to overhead, resource contention, and network saturation. Find the sweet spot for your infrastructure. Metrics show that exceeding CPU core count by more than 2x for concurrent test runs often leads to performance degradation.

By adhering to these best practices and proactively avoiding common pitfalls, you can harness the full power of parallel testing to accelerate your development cycles and deliver higher quality software.

Integrating Parallel Testing into Your CI/CD Pipeline

The true power of parallel testing is unlocked when it’s seamlessly integrated into your Continuous Integration/Continuous Delivery CI/CD pipeline.

This integration transforms testing from a sporadic, post-development activity into an intrinsic, continuous part of the software delivery process, ensuring quality at speed.

Think of your CI/CD pipeline as the central nervous system of your software factory, and parallel testing as the rapid feedback loop that keeps everything healthy and moving forward.

Why CI/CD Integration is Crucial

1. Automated Quality Gates: Parallel tests act as automated quality gates. Every time code is committed, merged, or a pull request is made, the CI pipeline automatically kicks off the parallel test suite. If tests fail, the pipeline can be configured to block the merge or deployment, preventing broken code from progressing.
2. Faster Feedback to Developers: With parallel execution, developers receive test results in minutes, not hours. This rapid feedback allows them to identify and fix issues immediately while the code is still fresh in their minds, drastically reducing the cost of bug fixes. Studies show that bugs found early in the SDLC are 10-100 times cheaper to fix than those found in production.
3. Continuous Validation: Ensures that new code changes don’t introduce regressions or break existing functionality. This continuous validation builds confidence in the codebase and allows for more frequent, smaller releases.
4. Efficiency and Resource Optimization: The CI/CD tool orchestrates the entire process, efficiently allocating test resources e.g., spinning up cloud machines or distributing tests to an on-premise grid. This maximizes the utilization of your testing infrastructure.
5. Reduced Manual Effort: Automating parallel test execution within the pipeline minimizes manual intervention, freeing up valuable time for testers and developers to focus on higher-value activities like exploratory testing or new feature development.

How to Integrate Parallel Testing Generic Steps

The exact steps will vary depending on your chosen CI tool Jenkins, GitLab CI, GitHub Actions, Azure DevOps, CircleCI, etc. and your test framework/platform. However, the general workflow remains consistent. Types of testing developers should run

1. Configure Your CI/CD Tool:

   • Define a Build/Test Job: Create a new job or stage in your CI/CD pipeline responsible for building the application and running tests.
   • Source Code Management SCM Integration: Configure the job to pull the latest code from your version control system Git, SVN upon every commit or pull request.
   • Dependency Installation: Ensure the CI agent/runner has all necessary dependencies Java, Node.js, Python, Maven, npm, pip, etc. and browser drivers installed if running on-premise.

2. Prepare Your Test Configuration for Parallelism:

   • Test Runner Configuration: Modify your test runner configuration e.g., testng.xml for TestNG, pytest.ini with pytest-xdist, Cypress configuration to specify parallel execution.
   • Cloud Platform Integration: If using a cloud testing platform Sauce Labs, BrowserStack, set up environment variables for your credentials username, access key. These variables will be picked up by your test code as shown in the previous section.

3. Execute Tests from CI/CD:

   • Command Line Execution: Your CI job will execute the command to run your tests. This could be a Maven command, npm script, Python command, etc.

   • Example Maven/TestNG with Sauce Labs: Download file using selenium python

     In your CI/CD script e.g., .gitlab-ci.yml, Jenkinsfile, .github/workflows/main.yml

     Set Sauce Labs credentials as environment variables securely

     export SAUCE_USERNAME=$SAUCE_LABS_USERNAME

     Export SAUCE_ACCESS_KEY=$SAUCE_LABS_ACCESS_KEY

     Build the project

     mvn clean install

     Run tests in parallel configured in testng.xml

     Mvn test -DsuiteXmlFile=testng-parallel.xml

   • Example Playwright/Cypress:

     Playwright: run tests in parallel on multiple workers

     Npx playwright test –workers=5 # 5 parallel workers

     Cypress: use a service like Cypress Cloud formerly Dashboard for parallelization

     or integrate with external orchestrators

     Npx cypress run –record –key –parallel –ci-build-id

4. Collect and Report Results:

   • Test Report Generation: Configure your test framework to generate test reports in a standard format e.g., JUnit XML, HTML, Allure Reports.
   • CI/CD Reporting Integration: Your CI tool should be able to parse and display these reports directly in the build UI. This provides a quick overview of test status pass/fail percentage, number of tests run and allows drilling down into individual failures.
   • Notifications: Configure notifications email, Slack, Microsoft Teams to alert relevant teams developers, QA immediately if the parallel test suite fails.

Advanced CI/CD Integration Concepts

• Conditional Execution: Run different sets of parallel tests based on the type of change e.g., run only unit tests on every commit, but full regression suite on nightly builds or before deployment to staging.
• Artifacts: Store test reports, logs, screenshots, and videos as build artifacts in the CI/CD system for later analysis and debugging.
• Test Retries: Some CI tools or frameworks allow for automatic retries of flaky tests. While it’s better to fix flakiness, a controlled retry mechanism can help reduce false negatives in highly parallel environments.
• Dynamic Parallelism: Advanced setups can dynamically adjust the number of parallel runners based on available resources or the size of the test suite.
• Blue/Green or Canary Deployments: In a fully automated CI/CD pipeline, successful parallel test execution can trigger phased deployments, where new versions are rolled out to a small subset of users before a full rollout.

By thoughtfully integrating parallel testing into your CI/CD pipeline, you create an automated, efficient, and robust quality assurance process that keeps pace with agile development and accelerates software delivery.

This systematic approach is critical for maintaining high software quality in competitive markets.

Measuring Success: Metrics and Monitoring for Parallel Testing

Just implementing parallel testing isn’t enough.

You need to know if it’s actually working as intended and delivering the promised benefits.

This is where metrics and monitoring come into play. Without data, you’re just guessing.

Effective measurement helps you optimize your setup, justify your investment, and ensure continuous improvement.

Key Metrics to Track

1. Overall Test Execution Time:

   • Definition: The total time taken for the entire parallel test suite to complete.
   • Why it matters: This is the primary indicator of the success of parallel testing. The goal is to drastically reduce this number compared to sequential execution.
   • Tracking: Measure the wall-clock time from the start of the first test to the end of the last test.
   • Target: A significant reduction e.g., 5x, 10x, 20x improvement depending on your parallelization factor. For instance, if sequential execution takes 8 hours, aim for it to finish in under an hour with parallelization.

2. Parallelization Factor/Efficiency:

   • Definition: Sequential Test Execution Time / Parallel Test Execution Time.
   • Why it matters: Quantifies the actual speed-up achieved. If your sequential run is 10 hours and parallel is 1 hour, your factor is 10x. It shows how effectively you’re utilizing parallel resources.
   • Tracking: Requires knowing the theoretical sequential time which can be calculated by summing individual test durations or running one sequential full run.
   • Target: As high as possible, ideally approaching the number of parallel threads/workers you’re running, though never 100% due to overheads.

3. Resource Utilization CPU, Memory, Network I/O:

   • Definition: The percentage of CPU, memory, and network bandwidth used by your test agents/nodes during parallel execution.
   • Why it matters:
     • Under-utilization: Means you’re paying for resources you’re not fully using especially in the cloud. You could potentially increase parallelism.
     • Over-utilization: Indicates bottlenecks. If CPU is consistently at 90-100%, tests might be slowing down due to resource contention or even failing. You might need more resources or fewer parallel threads per machine.
   • Tracking: Use monitoring tools specific to your infrastructure e.g., AWS CloudWatch, Azure Monitor, Prometheus/Grafana for on-premise, or dashboards provided by cloud testing platforms.

4. Test Flakiness Rate:

   • Definition: Number of test runs that fail sporadically / Total number of test runs * 100.
   • Why it matters: Flaky tests are a significant problem in parallel environments. They undermine trust in the test suite and lead to wasted debugging time. A higher flakiness rate indicates issues with test independence, environment stability, or test design.
   • Tracking: Monitor test history and identify tests that pass on one run and fail on another without any code changes.
   • Target: As close to 0% as possible. A healthy parallel suite should be deterministic. Google aims for less than 0.01% flakiness in their critical test suites.

5. Pass/Fail Rate:

   • Definition: Number of passing tests / Total tests run * 100.
   • Why it matters: While standard, it’s crucial to track in parallel testing. A sudden drop might indicate a serious regression, an environmental issue, or a critical flaw in your parallel setup.
   • Tracking: Most CI/CD tools and test reporting frameworks provide this out-of-the-box.

6. Cost of Execution for Cloud-based testing:

   • Definition: The financial cost incurred for running tests on cloud platforms.
   • Why it matters: Ensures you’re staying within budget and optimizing your cloud spend. High costs might indicate inefficient resource usage or excessive test runs.
   • Tracking: Utilize the billing dashboards provided by your cloud testing platform or cloud provider.

Monitoring Tools and Techniques

1. CI/CD Dashboards: Your CI/CD tool Jenkins, GitLab CI, GitHub Actions will provide the primary dashboard for viewing build status, test results, and often basic metrics.
2. Cloud Testing Platform Dashboards: Services like Sauce Labs, BrowserStack, and LambdaTest offer rich dashboards with:
   • Real-time test execution views.
   • Detailed logs, screenshots, and video recordings of test runs.
   • Aggregated test results by browser/OS/device.
   • Performance metrics for each test.
   • Billing and usage reports.
3. Dedicated Monitoring Tools:
   • Prometheus & Grafana: Open-source tools for collecting and visualizing metrics from your test infrastructure if on-premise.
   • ELK Stack Elasticsearch, Logstash, Kibana: For centralized logging and log analysis, crucial for debugging distributed parallel tests.
   • APM Application Performance Monitoring Tools: If your tests involve hitting your application’s backend, APM tools e.g., Dynatrace, New Relic, Datadog can monitor the application’s performance under parallel test load.
4. Custom Dashboards: For advanced insights, you might build custom dashboards using data collected from various sources, tailored to your team’s specific needs.

By consistently monitoring these metrics, you can gain valuable insights into the efficiency and effectiveness of your parallel testing efforts.

This data-driven approach allows you to identify bottlenecks, troubleshoot issues, and continually refine your strategy, ensuring that parallel testing truly delivers on its promise of faster and more reliable software delivery.

The Future of Parallel Testing: AI, Smart Orchestration, and Beyond

As applications become more complex, development cycles shrink, and the demand for instant feedback grows, parallel testing will continue to innovate, leveraging emerging technologies to become even more intelligent and efficient.

1. AI and Machine Learning for Smart Test Orchestration

This is perhaps the most exciting frontier. AI/ML can move parallel testing from “run everything concurrently” to “run the right tests concurrently, in the optimal way.”

• Predictive Test Selection: AI can analyze historical test data code changes, past failures, execution times to predict which tests are most likely to fail given a new code commit. Instead of running the entire suite, only the high-risk tests or impacted tests could be prioritized and run in parallel first, providing even faster feedback. Companies like Google already use intelligent test selection to run only a fraction of their massive test suites for daily commits, significantly reducing execution time.
• Dynamic Parallelism Adjustment: ML algorithms can monitor real-time resource utilization and test queue lengths to dynamically adjust the number of parallel workers or optimize test distribution across the grid. This ensures optimal resource allocation, preventing bottlenecks and maximizing throughput.
• Flaky Test Detection and Root Cause Analysis: AI can identify patterns in test flakiness, suggesting potential root causes e.g., shared resources, timing issues, specific environmental configurations. This moves beyond manual identification to automated insights, accelerating the resolution of these trust-eroding tests.
• Self-Healing Tests: Though still in early stages, AI could potentially analyze test failures and suggest minor adjustments to test scripts to “self-heal” them, reducing manual maintenance efforts.
• Smart Test Data Generation: AI can generate synthetic, realistic, and unique test data on demand for parallel test runs, ensuring data independence without manual effort.

2. Enhanced Cloud-Native Test Infrastructure

The reliance on cloud platforms will only deepen, leading to more sophisticated cloud-native testing solutions.

• Serverless Test Execution: Leveraging serverless functions AWS Lambda, Azure Functions to run individual tests or small test groups, providing ultimate elasticity and “pay-per-execution” billing models. This means you only pay precisely for the compute time of each test.
• Kubernetes for Test Orchestration: Kubernetes will become even more central for managing large-scale, distributed test environments. Its ability to manage containers, scale services, and handle self-healing will make it an ideal platform for running massive parallel test suites.
• Edge Device Testing: As IoT and edge computing proliferate, parallel testing will extend to a wider array of physical and virtual edge devices, ensuring functionality across highly distributed architectures.

3. Shift-Left and Shift-Right Testing Paradigms

Parallel testing will continue to integrate more deeply into the entire software development lifecycle.

• Shift-Left with Developer-Driven Parallel Testing: Developers will increasingly run parallel tests on their local machines or in pre-commit hooks, catching issues even before code is pushed to the central repository. Tools will make this more seamless.
• Shift-Right with Production Monitoring and Observability: Insights from production monitoring can feed back into parallel test strategies, informing which tests are most critical to run frequently in parallel to prevent real-world issues. Parallel testing can also be used for A/B testing or canary deployments, testing new features on a subset of users in parallel to the stable version.

4. Specialized Test Types in Parallel

As testing broadens, so will the application of parallelization to other domains.

• Parallel Performance Testing: Running multiple load tests concurrently against different API endpoints or application components to quickly identify performance bottlenecks.
• Parallel Security Testing: Executing multiple security scans e.g., static analysis, dynamic analysis against different parts of the code or application simultaneously.
• Parallel Accessibility Testing: Running automated accessibility checks across various browser/device combinations in parallel.

5. Low-Code/No-Code Parallel Testing Tools

As automation becomes more accessible, tools that abstract away the complexities of setting up and managing parallel execution will emerge, empowering a wider range of users to leverage this technique without deep technical expertise.

These tools will offer intuitive interfaces for configuring test environments and parallel runs.

The future of parallel testing is intelligent, highly automated, and deeply integrated into the development process.

It will continue to be a cornerstone of rapid, high-quality software delivery, adapting to new technological paradigms and pushing the boundaries of testing efficiency.

Frequently Asked Questions

What is parallel testing in software testing?

Parallel testing is a software testing technique where multiple tests or test cases are executed simultaneously on different environments, machines, or configurations.

It aims to significantly reduce the overall test execution time by leveraging concurrent processing, rather than running tests one after another.

What are the main benefits of parallel testing?

The main benefits include drastically reduced test execution time, faster feedback to developers, enhanced test coverage across various environments browsers, OS, devices, optimized resource utilization, and improved efficiency of CI/CD pipelines, ultimately leading to faster delivery of higher-quality software.

How does parallel testing differ from sequential testing?

In sequential testing, test cases are executed one after another, in a linear fashion.

In contrast, parallel testing executes multiple test cases or suites concurrently, significantly cutting down the total time required to complete the testing cycle.

Is parallel testing suitable for all types of applications?

It’s particularly beneficial for web, mobile, and API testing.

What are the key prerequisites for successful parallel testing?

Key prerequisites include independent and atomic test cases no shared state, a robust test automation framework that supports parallelization, a scalable test infrastructure cloud or on-premise, efficient test runner configuration, and seamless integration with a CI/CD tool.

What tools are commonly used for parallel testing?

Commonly used tools include Selenium Grid for distributed browser testing, cloud-based testing platforms like Sauce Labs, BrowserStack, and LambdaTest, and CI/CD tools like Jenkins, GitLab CI, GitHub Actions, and Azure DevOps for orchestration.

Test frameworks like TestNG Java, JUnit 5 Java, and pytest-xdist Python also support parallel execution.

Can I do parallel testing without a cloud platform?

Yes, you can set up an on-premise Selenium Grid or leverage multi-threading capabilities within your test framework on a single machine.

However, managing on-premise infrastructure requires significant effort in terms of setup, maintenance, and scalability compared to cloud solutions.

What is cross-browser parallel testing?

Cross-browser parallel testing is a type of parallel testing where the same test suite is executed concurrently across multiple web browsers e.g., Chrome, Firefox, Safari, Edge and their different versions, ensuring the application functions consistently across all of them.

What is cross-platform parallel testing?

Cross-platform parallel testing involves running the same tests simultaneously on various operating systems e.g., Windows, macOS, Linux and/or mobile device platforms e.g., iOS, Android to ensure compatibility and consistent behavior across different environments.

How do I manage test data in a parallel testing environment?

Test data management in parallel testing requires careful planning.

Best practices include generating unique test data for each test run, using dedicated test users, resetting test data before each test, or employing database snapshots/rollbacks to ensure data isolation and prevent conflicts between concurrent tests.

What are common pitfalls to avoid in parallel testing?

Common pitfalls include ignoring test dependencies, underestimating infrastructure needs, poor test data management, inadequate reporting, neglecting flaky tests, and over-parallelization, which can lead to resource contention and diminishing returns.

What is test flakiness, and how does it relate to parallel testing?

Test flakiness refers to tests that produce inconsistent results pass sometimes, fail other times without any changes to the code.

Parallel testing can amplify flakiness if tests are not truly independent, leading to unreliable results and eroding trust in the automation suite. It’s crucial to identify and fix flaky tests.

How does parallel testing impact CI/CD pipelines?

Parallel testing significantly enhances CI/CD pipelines by providing faster feedback loops, enabling automated quality gates, allowing for continuous validation of code changes, and reducing manual effort, thereby accelerating the entire software delivery process.

Can unit tests be run in parallel?

Yes, unit tests are often very atomic and independent, making them excellent candidates for parallel execution.

Many unit testing frameworks e.g., JUnit, NUnit, Jest support parallel execution, allowing developers to get extremely fast feedback on their code changes.

Is parallel testing considered a performance testing technique?

No, parallel testing is primarily a functional testing technique focused on efficiency and coverage by concurrent execution.

While it can identify performance bottlenecks if the application struggles under concurrent test hits, it’s not a substitute for dedicated performance or load testing.

How many parallel tests should I run?

The optimal number of parallel tests depends on your available infrastructure resources CPU, RAM of your test machines/cloud capacity and the nature of your tests.

There’s a sweet spot where increasing parallelism yields diminishing returns due to overhead.

Start with a conservative number and gradually increase while monitoring resource utilization.

What are “shared resources” in parallel testing, and why are they a problem?

Shared resources are elements that multiple tests try to access or modify simultaneously, such as a single database record, a specific API endpoint with rate limits, or a file on a shared drive.

They are a problem because they can lead to race conditions, data corruption, and unpredictable test failures if not managed carefully.

How do I debug failures in a parallel testing environment?

Debugging parallel test failures can be challenging.

It requires robust logging, clear reporting that indicates which test failed on which environment, and access to artifacts like screenshots and videos.

Centralized logging tools and cloud platform dashboards are invaluable for efficient debugging.

Does parallel testing require more complex test scripting?

Not necessarily.

The test scripts themselves typically don’t change much.

The complexity lies more in setting up the parallel execution environment, ensuring test independence, and managing test data, rather than in the individual test steps.

What is the future outlook for parallel testing?

The future of parallel testing involves increasing adoption of AI and Machine Learning for smart test orchestration predictive test selection, dynamic resource allocation, enhanced cloud-native infrastructure serverless testing, Kubernetes, deeper integration into shift-left and shift-right testing, and expansion into specialized testing types like performance and security testing.

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