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According to the Redgate 2019 State of Database DevOps report, 65% of companies use production data for testing, and only 35% use masking techniques. The cybersecurity risk for these scenarios is significant, highlighting the importance of proper test data management. Meanwhile, as software becomes more complex with multiple layers and subsystems, the need for quality data rises in tandem. Quality data is essential to ensure all components work together as expected. This guide will discuss some modern testing strategies and the importance of quality test data to ensure proper test coverage.
Companies are embracing continuous integration and delivery to meet market demand and provide customers with exceptional digital experiences. The need for speed is key. But traditional monolithic applications significantly hinder the process. These applications rely on tightly coupled components that are hard to test in isolation and make the application very fragile. In other words, a change in one component will likely have significant impacts on every other area of the system.
The service-based application architecture evolved out of a need to de-couple application components to eliminate code fragility and allow for more rigorous component testing without negatively affecting other areas. This new approach requires strategies to ensure that every layer of the system works as expected and interfaces correctly with any third-party systems.
These new strategies are categorized into “buckets” that indicate the level of granularity required at each stage. The stages are often referred to as the testing pyramid and form the basis of the entire test suite for an application.
The Unit test, at the bottom of the pyramid, is the most granular test one can perform. The test is used to validate small chunks of code, typically a single function. It is a white box technique developers perform during coding to isolate functions and rigorously test them to ensure they work properly. Isolating functions in this way helps identify unnecessary dependencies between the unit being tested and other components of the system. Several tools are available to assist with this type of validation, such as JUnit, PHPUnit and JMockit.
After verifying that each isolated unit works as expected, the next step is to ensure that these units work as expected when grouped. The goal at this stage is to expose defects in the interfaces and integrations between components. Selenium, JUnit, Mockito and AssertJ are just a few examples of tools used for this type of evaluation.
With the evolution of service-based architecture, testing the service endpoints and verifying that the API works as outlined becomes crucial. This type of verification tests each interaction scenario with the API endpoint using tools like Apache JMeter, Jaeger or HoverFly.
The user interface is the customer's first impression of the application. Whether or not the system works won't matter if it isn’t user-friendly. UI verification checks to ensure that the visual elements work as expected and allow the user to accomplish the tasks they need to perform. These types of tests include all user actions carried out via the keyboard, mouse or other input devices. They also check to ensure all UI elements display correctly. A few standard tools for performing UI tests include Katalon, Selenium IDE and Testim.
As the name indicates, end-to-end testing is a technique that validates the entire application from beginning to end to ensure that it works properly. The purpose is to evaluate the entire system for dependencies, data integrity, and interfaces to databases or other systems in a production-like scenario. Cypress, Cucumber and Selenium are a few of the tools often used at this stage.
The true indicator of success is whether the application meets the user’s needs as defined in the requirements. This stage is where actual end-users work in the system performing real-world scenarios to evaluate the system.
It is not always possible to come up with every scenario when planning test cases. According to TechBeacon, “Exceptional and experienced testers have an instinctive manner in which they find defects.” Therefore, it is instinct, experience and knowledge that helps testers explore and uncover defects that may not have been otherwise detected.
Testing is only half the battle. Generating optimal test data can decrease ramp-up time to begin system validation and increase the likelihood of detecting bugs by relying on data that closely resembles production.
Another significant importance of having appropriate data generation is to ensure compliance with GDPR. Using advanced de-identification and anonymization techniques can help ensure your company does not unintentionally violate privacy laws.
Tonic helps you strengthen your staging environments with useful, realistic, safe data created from your production data. Using this data to hydrate all of your lower environments can help shorten sprints and deploy releases up to five times faster. Request a demo to learn how we can help enhance your test suite.