Synthetic testing or synthetic monitoring, involves simulating real user traffic to find performance issues in key user journeys. Companies use this type of testing to actively monitor service availability, application response times, and the functionality of customer transactions. This guide covers everything you need to know to perform synthetic testing. Let's begin.
Synthetic testing is a software testing approach that involves simulating real-world scenarios to evaluate the performance of a system or application. It is becoming increasingly important in today's software development because enterprises need to deliver software faster while ensuring high quality and reliability. Here are some of the reasons why:
Synthetic testing is typically an automated technique used to evaluate the performance and functionality of a software system or application. It simulates user behavior by creating virtual requests and measuring the response time, scalability, and other performance indicators. Synthetic tests are created using scripts defining user actions, such as clicking links, filling in forms, and checking response times. These scripts can be run repeatedly to check the consistency of the test results.
However, synthetic testing can be conducted manually, especially during the initial stages of testing or when verifying specific user journeys or edge cases. Manual testing may validate automated test results or assess scenarios requiring human judgment or creativity.
Let’s explore the benefits of synthetic testing in more detail.
By conducting synthetic testing early in the software development lifecycle, developers can identify and address potential issues before they become significant problems. Thus, synthetic testing can lead to high-quality and reliable software that meets user expectations.
When simulating real-world scenarios across different geographic locations, companies can ensure their applications are ready for use in new markets. This preparation allows for confident expansion, minimizing the risk of application failures or downtime.
Synthetic testing detects potential performance issues and ensures code changes don't negatively impact application performance. This facilitates frequent code deployment, allowing companies to introduce new features and updates rapidly while maintaining software quality and reliability.
Quickly detecting and diagnosing performance problems helps developers resolve issues more swiftly. This reduces the mean time to resolution (MTTR), minimizing downtime and keeping applications available and operational.
Maintaining optimal application performance under different conditions is key to meeting performance standards consistently. That way, companies can deliver high-quality and reliable software, giving them a competitive edge.
Establishing performance benchmarks and optimizing system performance allows companies to meet performance targets effectively. Also, it ensures that applications remain performant even under heavy traffic loads.
Synthetic testing fosters agile development practices by enabling continuous testing and quick, reliable feedback to developers. This enhances software quality while enabling faster releases and shorter development cycles.
Despite its benefits, synthetic testing also presents several challenges that should be addressed to ensure optimal results.
Creating test cases that cover all possible user actions and scenarios can be challenging, particularly for complex applications. One way to address this challenge is to use capture and playback tools that record user actions and convert them into test cases. However, this approach may not reflect the behavior of actual users, and the test cases could miss critical scenarios.
Test scripts should be updated regularly to reflect changes in the application or system under test. For instance, a change in the user interface or application flow may require modifications to the test scripts. Failure to update the test scripts can lead to errors and inaccurate results.
Synthetic testing can struggle with dynamic content, such as generated data that changes frequently and needs to be captured correctly in test cases. For example, suppose an application generates user IDs, transaction numbers, or timestamps. In that case, synthetic tests must account for such dynamic content to ensure accuracy.
The quality and accuracy of the test data can impact the results of synthetic testing; thus, the management and organization of test data are crucial. The test data used in synthetic testing should practically represent the actual data used in the live environment. Poorly managed test data can lead to test case failures and errors.
Synthetic testing should be integrated with other tools, systems, and workflows for automated continuous testing. Integration with other systems can provide a better view of the application's performance and enhance the accuracy of testing results. However, integrating multiple test tools can also present challenges, such as compatibility issues, data storage problems, and management problems.
The two main classes of synthetic testing are:
Synthetic testing and real user monitoring (RUM) are two approaches to evaluating a software application's performance. Synthetic testing involves simulating user actions and behavior to test the application's functionality, performance, and security. In contrast, RUM involves monitoring interactions with the application in real time.
Several requirements are essential for performing synthetic testing effectively.
Here are 15 synthetic testing best practices that you can consider:
Objective: To test the entire user login process, from entering credentials to accessing the account dashboard.
Steps:
Expected result: The application should authenticate the user and provide access to the account dashboard without any delays or errors.
Objective: To ensure the checkout process in an e-commerce application functions smoothly and accurately.
Steps:
Expected result: The application should successfully process the purchase and provide a confirmation message and receipt to the user.
Objective: To assess the responsiveness of an API by measuring the time it takes to respond to requests.
Steps:
Expected result: All API endpoints should respond within acceptable time frames, ensuring a smooth experience for users relying on the API.
Global App Testing excels in synthetic testing through its extensive network of skilled testers and broad device coverage. Its user-friendly platform efficiently identifies and resolves app bugs, providing insights and reports that enhance testing. Moreover, the platform offers distinctive features that further elevate the value of synthetic testing:
Easy-to-use platform:
Fast and dependable services:
Worldwide user feedback:
Quality assurance:
And extra:
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