Codeless test automation relies on record and playback to create accurate test automation. Testers who have been in the QA testing world for ten-plus years have likely experienced disappointment with record and playback tools for test automation. Many actually took so much to run, users couldn’t use any other applications. Or, you could record and the tools were unable to recognize objects without developers having to code in hooks. Take heart, though, because record and playback tools in 2024 have improved via artificial intelligence (AI) and machine learning (ML) technologies.
Take a fresh look at record and playback tools in codeless or low-code testing tools. Automated testing tools have evolved and will continue to evolve with AI and ML features. Now’s the time that using a record and playback tool may offer the advantages promised in the 90s. AI and ML are test automation’s saving grace, making it feasible to create effective and valid test automation without being an experienced coder.
This guide describes the challenges of using record and playback tools and the benefits of using AI and ML technology to improve testing.
Record and Playback Trials and Tribulations
Using record and playback to create automated tests without requiring coding knowledge is old news. The tools have been around for decades with the same painful issues:
- When anything changed on the UI (user interface) the execution fails.
- Tests are difficult or impossible to edit so had to be re-recorded from scratch.
- Complex features or test scenarios were beyond the capacity of these tools so remained as manual test executables.
Previous to AI and ML, QA engineers without coding knowledge created automated tests using these record-and-playback tools. However, these tests were brittle and would easily break: when the most simple change occurs to an object, either a locator change or perhaps where it is within the HTML object model, a recorded test will consider this to be a bug and report a failure. The QA engineer naturally must examine every failure reported by their scripts, and the locator (or other issue at cause) must be edited. Depending on the tool you were using, this editing ability may not even be possible! If not, the entire script must be re-recorded in order to remain relevant.
One additional nail in the coffin for manually recorded testing tools is the simple fact that these often do not have the ability to interact with modern complex websites. Many tools won’t be able to recognize inline frames (iFrames), graphic overlays, and other dynamic elements common to current web apps. Why choose a tool that limits your ability to automate your primary test scenarios, and limits your ability for thorough test coverage?
In short, recorded scripts are prone to failure and require a high maintenance approach to test automation. Automated testing projects utilizing this strategy are either limited in scope or failed entirely because the time required to maintain the tests was far greater than executing the tests manually. In some instances the QA teams would re-create the tests by re-recording them every sprint rather than edit them.
Codeless test automation tool improvements
Codeless and no-code test automation tools are (at the time of this writing) hot. Most are evolved record and playback tools with added features and more robust script creation. Systems are no longer missing editing features that can assist in creating scripts with checkpoints or added complexity.
AI and ML technology have created tools where the script looks like a well-written manual test case. Users can edit and maintain tests or use self-healing options.
Additionally, current record and playback tools offer wider support for identifying objects, support menu selection, hover, and drag-and-drop actions. No more needing a developer or SDET to edit recorded scripts.
In the past, using a playback and record tool consumed a user’s computer resources. Users couldn’t use any other applications when recording tests because the tool ate up the CPU. Modern tools allow users to continue to use other applications with the record and playback tool open.
More Benefits of AI/ML for Test Teams
AI and ML technology are what test automation tools have been missing. Tools using AI and ML allow QA testers to create effective test automation without relying on others with coding knowledge. In other words, using automated testing rapidly and consistently is closer to reality than in previous decades.
AI and ML improve testing by:
- Assisting with script development
- Creating efficient reporting options
- Expanding test coverage into more complex test scenarios
- Enabling rapid test development without redoing work
- Using object recognition to find objects automatically
- Visual testing with computer vision to catch UI changes automatically without scripting
- Using optical character recognition (OCR) to self-heal or correct test scripts
- Performing automated test data creation
Another key improvement with AI and ML test automation tools is ML can quickly generate accurate and scrubbed data sets for testing. No more painfully trying to create test data manually or refreshing the same developer-created data for testing. ML can learn production data, de-identify it for security, and generate it for testers on request. Imagine the time saved.
The technology is not there yet, but the future of automated testing is autonomous. The way to prepare as a tester or a testing team is to use the technology available now. Learn how it works, find its weaknesses, and create solutions to support corrections. AI and ML will likely always need monitoring because they use data that may or may not be correct. Human testers are necessary to enforce accuracy and quality, but that shouldn’t keep test teams from benefiting from test automation.
Early adaptation of AI and ML technology allows testers to be involved in the next phase of test automation. Testing teams can use technology now to optimize testing by increasing testing’s business value, speed, coverage, and accuracy. Learn now how AI and ML technology impact testing and make the most of both to build quality test automation that’s valid, fast, and easy to maintain.
