In a perfect world, end-to-end testing simulates how software will perform with real users from start to finish.
With end-to-end testing teams face off against low testing cycles and high levels of complex relationships, all of which threaten the quality and speed of deployment.
As services and code expand, so do the number and variety of tests. Test cases grow alongside dead code. The key to successful end-to-end testing is tackling bloated test suites and eliminating flaky tests.
That’s why DevOps teams are tackling end-to-end testing with a dual approach: using AI-driven predictive test selection and flaky test insights.
Modern applications continue to be more dispersed and complicated year after year, and as a result, other systems have a significant role in how well they perform. DevOps teams are not just responsible for ensuring that a standalone program works from end-to-end but that it successfully interfaces with other systems, including databases, networks, and hardware.
End-to-end testing is a crucial part of the software development lifecycle (SDLC) that helps ensure your application is working as expected, from end-to-end. By simulating real user interactions, you can catch bugs and issues before they impact your users. But with end-to-end testing teams face off against low testing cycles and high levels of complex relationships, all of which threaten the quality and speed of deployment.
End-to-end (E2E) testing is a software testing approach that verifies the flow of an application from start to finish. In a perfect world, these tests are set up to simulate how the software will perform with real users from start to finish. They help ensure that all the working parts work together as one cohesive unit.
Without end-to-end testing, there's no way to know if all components are connected correctly or if data is processed correctly. Additionally, E2E tests provide insight into how users interact with the system, which can be used to improve user experience.
By simulating real user interactions, you can catch bugs and issues before they impact your users. In a nutshell, end-to-end testing is a crucial part of software development that can help ensure your application is working as expected.
As we said before, adding end-to-end testing to your testing lifecycle helps ensure your application is covered and vetted before being shipped to production. We all know how creative users can be when it comes to finding bugs, and with end-to-end testing, your QA teams can be better prepared to handle issues before your customers do.
Testing back-end functionality: End-to-end tests evaluate whether the back-end and database layers carry out crucial operations as planned in addition to testing front-end functionality.
Validating behaviors in complex systems: In addition to evaluating a program's overall functionality, end-to-end testing can determine whether each layer functions correctly.
Assuring user experience: End-to-end testing enables assessing an application's functionality and consistency of the user experience across numerous platforms, environments, and devices.
Finding problems in distributed environments: Applications are often widely dispersed as orgs depend more and more on cloud services. End-to-end testing identifies issues with communication between these dispersed environments.
Teams that don’t use end-to-end testing often struggle to connect all of their individual working parts for a final product. Applications are complex, often with multiple components that can be difficult to ensure data streams correctly to each piece.
Plus, end-to-end tests help teams ship to market faster. With automation and tools like Predictive Test Selection, QA teams can set up user paths to test critical features. These tests can also save on resources by speeding up the testing process once the initial setup has been done.
We’re all familiar with the old verbiage “nothing is perfect,” and that includes standard approaches to end-to-end testing. Let’s go over some of the common challenges DevOps teams encounter in the field when running end-to-end tests:
Tests can be hard to design: Without a deep understanding of your application and the different user paths you may need to test for, tests can either cover too much or not enough. This leads to a lot of time spent at the beginning of end-to-end testing but can be reduced dramatically with time.
You need to understand your users: Since end-to-end testing intends to behave like a user, you’ll need to know how they will act. This generally means you’ll need to analyze how your users handle solving a problem, which means you might need to collect more user analytics or set up beta testing.
End-to-end tests are high maintenance to maintain: End-to-end tests are plain tough to maintain. This is due to the fact that the test must be updated each time an element of the program changes, which could entail a significant number of modifications. Prioritizing certain workflows above others based on risk is one of the most effective methods to address maintenance obstacles so that QA teams aren't overburdened with designing and rewriting end-to-end tests for every potential workflow.
Flaky tests erode confidence in end-to-end test outcomes: End-to-end tests are frequently "flaky" because they are designed to imitate real-world events, in addition to general maintenance difficulties. Flaky tests drain developer resources, as failed tests require developer time to analyze and retest and erode the confidence in the tests themselves.
As services and code expand, so do the number and variety of tests. Test cases grow alongside dead code, tests you had no idea when they came in and tests you have no idea what they are testing.
These issues have a negative impact on orgs and overall developer experience and productivity for two reasons: Turnaround time and cost for unnecessary tests in your CI/CD and developer resources and motivation to keep unnecessary-looking tests running.
That’s why DevOps teams tackle end-to-end testing with a dual approach, using AI-driven test selection and flaky test insights.
The key to successful end-to-end testing is tackling bloated test suites and eliminating flaky tests. Launchable is the powerful dev intelligence platform teams use to speed up their development feedback loop with data-driven testing insights. Using machine learning, Launchable optimizes CI pipelines by identifying and running tests with the highest probability of failing and tracking test suite insights – including flakiness probability – to cut developer wait times.
Launchable helps your QA teams select the best tests to run by analyzing code changes and usage data. This saves your developers precious time and allows them to focus on the more critical tests and ensure you have maximum coverage. They get feedback earlier in the testing cycle, which can help improve tests from the start, so end-to-end tests are set up for success.
End-to-end testing will only continue to become more complex as your software expands in features and integrations. End-to-end tests are essential to any software testing life cycle and can show your teams some of the most significant flaws before production. While getting started may be a struggle, implementing end-to-end testing doesn’t have to be a scary ordeal. And with Launchable, speeding up your test cycles and the feedback loop is easier than ever.