Low-code and no-code development platforms have revolutionized how companies think about application development. The same can be said for software for quality assurance (QA), where codeless test automation solutions handle the burden of coding for organizations that can’t allocate extra programming resources or keep up with extensive writing and maintenance of automated tests.
Codeless test automation can be a big win for companies that are resource-challenged and trying to get applications to market quickly. A recent Applause survey of more than 2,000 digital experts revealed that nearly 54% of organizations have strategic plans to purchase a codeless test automation tool in the future.
Despite the heat in the market, though, it is not a fit for every type of application or use case. It is not, in most cases, a replacement for scripted automation, but rather should be used as a complement to traditional automated testing and manual functional testing.
An example of this is with dynamic content in applications, which have unpredictable or even subjective outcomes. Consider a streaming media platform. An automated solution might validate that the “Play” button triggers the code to play a movie, but it cannot validate that the movie plays without audio or video glitches.
In complex cases that aren’t a fit for either codeless or traditional automated methods, manual testing is a better alternative.
Test automation is not going anywhere, even as codeless tools become more prominent. That’s because automation enables brands to move faster and meet the fast-moving target of building and testing software that is both high quality and able to be released to end users quickly. The downside of traditional test automation, however, is that building and maturing the process can be time-, cost- and resource-intensive.
This is because traditional automation requires experts in coding, not only to write tests at the outset but to also maintain them over time. The complexity and need for different tools—like Appium, Selenium, Apple simulators, Android emulators, element locators, locator strategies and so on—all contribute to the complexity of traditional automation and the need for specific expertise. Plus, software development engineers in test (SDETs) are constantly being taken from QA teams and moved into development roles, which often means that these projects get stuck halfway through and never fully completed, which can then result in a tremendous waste of both time and money for companies.
The overarching value of codeless test automation, on the other hand, is that anyone can do it. With a codeless solution, a user simply moves through a test case and the codeless tool can then transcribe that experience into an automated test. And, while codeless test automation tools originally addressed only web applications, more tools now offer the ability to run sessions and create automated tests on mobile apps—on both Android and iOS—as well as web applications.
What it all comes down to, though, is that organizations shouldn’t be thinking of codeless tools and traditional automation as an either/or scenario. Codeless test automation tools are great for less-involved scenarios—like smoke tests or portions of a regression testing suite. Using codeless tools in this way allows SDETs and dedicated automation resources to focus on higher-priority and more complex automation. Ideally, manual testing should be used alongside both traditional automation and codeless test automation solutions as a way to maximize the speed, scale and quality at which software can be delivered to end users.
As a rule of thumb, every organization should balance test automation and codeless test automation and consider automating when the following factors are in play:
The data used to train AI models needs to reflect the production environments where applications are deployed.
Looking for a DevOps job? Look at these openings at NBC Universal, BAE, UBS, and other companies with three-letter abbreviations.
Tricentis is adding AI assistants to make it simpler for DevOps teams to create tests.
Redis is taking it in the chops, as both maintainers and customers move to the Valkey Redis fork.
GitLab Duo Chat is a natural language interface which helps generate code, create tests and access code summarizations.