Strategies to Scale Automated Test Suites
Modern applications need scalable test automation. Strategies such as using a test cloud, driving down test debt and intelligent test automation will help scale your automation practice and drive efficiencies.
Test automation looks very different than it did as little as ten years ago. Testing used to be a specific siloed process at the end of the development lifecycle. The test suite would consist of a finite number of test cases, which would cover all the possible scenarios for end users. Now, that number is not so finite. To bring modern applications to market, test suites have to process thousands of tests in less and less time. Speed and efficiency are competitive drivers for software products today. Test automation has a large part to play.
Cloud computing and machine learning technologies are making resources available to testing teams that have previously relied heavily on skilled software engineers exhausting their time on manual creation of test scripts. This approach is no longer scalable for the volume and complexity of testing that’s needed - not to mention the budgets that they demand.
In this article, we go over a few modern strategies to scale automated testing suites.
1. Use a test cloud to benefit from the size and complexity of a virtual environment
Developers and testers both know all too well this situation: “It’s working fine on my machine!” Physical machines are susceptible to environment-related discrepancies. As a result, testers see mixed results in different instances. Running tests in the cloud is much more scalable than running tests on your local machine.
Test execution is subject to your local machine’s processing power and memory. If your company hasn’t had a hardware refresh in a while, you may be unfortunate enough to have a machine whose CPU or RAM gets overloaded with the dozens of apps and tools you are using for your day-to-day activities. How would it handle an instance of a thousand test runs? Chances are it would interfere with the way the application behaves under testing or stall the testing process altogether.
Also, setting up test machines and all of the supporting infrastructure is cumbersome and labor-intensive. It is not scalable for modern test practices.
When you run tests in the cloud (for example, the Functionize Test Cloud), you can see how the application under test behaves in a clean virtual machine free of environmental disruptions.
Cloud models help the environment scale capacity elastically as your project needs. It enables you to run thousands of tests in parallel, further widening the scope and coverage. And the resources are managed by the provider, which means easy infrastructure maintenance and no execution discrepancies compared to a local test environment.
2. Use AI and ML to reduce test debt
Most testing teams struggle to keep up with business demands due to the insidious cycle of test debt. The most significant form of test debt is the ongoing maintenance of automated tests.
Legacy automated testing platforms only offer automation of test execution. They rely on hardcoded selectors. The actual scripting of test cases needs to be done manually. Every time your application UI is updated, some of your automated tests break and need to be fixed manually by test engineers or developers before they can be run again. The larger and more complex an application gets (i.e., the more features and fixes your end users demand) the more manual test scripting it needs. This leads to testing teams falling behind the pace of new development. Instead of focusing on value-adding tasks, they are forced to make painful trade-offs between quality, release speed and costs.
In our pursuit of breakneck-speed releases and awe-inspiring digital transformation, we often overlook a simple metric that drives scalability. Time.
Fixing tests that break with each application change only to have to fix them again with the next change is a vicious cycle that drains time spent by scarce skilled resources. Test debt limits scalability. It simply holds you back, no matter what your automation goals are.
AI, ML and big data represent the future for test automation and the effective management of test debt. Functionize uses machine learning and big data to tackle test debt. Instead of hardcoded static selectors, Functionize uses millions of data points to accurately recognize UI elements and track relevant changes. Tests can self-heal, so the manual scripting activity is either eliminated or greatly reduced. This not only helps improve end-to-end test coverage but also enables teams to avoid crushing test debt.
3. Use intelligent test automation to integrate testing with other modern DevOps tools and workflows
Modern application development uses approaches like DevOps and CI/CD to develop, test and deliver faster. These approaches started out as agile and collaborative approaches to development, so that features and fixes could be rolled out as fast as their ever-growing consumer bases need them. However, testing has not quite caught up to innovation in development yet. Test automation still relies on manual scripting of test cases and often becomes the bottleneck in the delivery process.
Intelligent test automation helps you move away from this approach. Functionize uses AI and ML to keep collecting data and keep learning more about your application. Your test definition is an ever-expanding dataset from every execution.
Functionize’s modern testing approach uses features such as Smart Waits, Smart Scrolling and computer visions to optimize time and computing resources spent on tests. Machine learning allows the system to adapt intelligently to change, rather than needing extensive, complex customization and time-consuming hardcoding of test scripts. When you update your application, your tests self-heal and free up time to create more automated tests.
Integrating intelligent test automation with other modern DevOps tools will help you scale your test automation without worrying about clogging up the delivery process. If you can intelligently scale your functional testing, then you can integrate with CI/CD pipelines. Functional tests can be shifted left, which means that they can run earlier and more often. This completely eliminates any need to silo the testing process. It also greatly reduces the painstaking work of fixing brittle test scripts that traditional test automation brings.
An intelligent testing solution also offers test management integrations for holistic test planning and consolidated reporting. With this seamless approach, engineering leadership is charged with innovation and strategy development. It is their job to implement, optimize and scale automation, not adopt it just for the sake of it. Intelligent testing, when deployed in tandem with strategic test management, helps them track progress of overall quality, especially necessary in larger enterprises for executive reporting.
To conclude, we are moving into an era in which we have modern testing technologies to match up to the innovation in software development. Strategies such as using a test cloud, driving down test debt and intelligent test automation will help scale your automation practice and drive efficiencies. Book a demo now to learn more about how to scale your automated testing suite.