In this post, we feature a comprehensive article on 3 Features To Look For in a Codeless Automation Testing Tool.
Codeless Automation Testing Tools
With the growing need to keep up with faster release cycles, QA teams have turned to codeless automation tools to speed up the test automation process. When implemented successfully, testers can ensure that their work is both high-quality and high-quantity to support their companies’ agile processes.
There are many tools on the market that claim to be codeless. Yet plenty fall short when it comes to making test creation and execution a simpler process. Plus, these software solutions can more often only help with simpler test scenarios. Tools that aren’t truly codeless will cause problems at scale.
To better sift through the different tools on the market, here are the three features to look for when investing in a codeless test automation tool. Keeping these features in mind will help you determine if these tools live up to the hype and will help your team better reach its objectives.
“Record and Playback” vs. Modeling
For codeless automated testing tools, it is important to look into how the solution actually creates your tests. Many tools that say they are codeless are often test recording tools, or “record and playback” tools. Capturing user actions in real time to create new test flows, test recorders are fast and easy to use. This makes them a very appealing test automation option with a minimal learning curve.
However, test recorders also have their limits when it comes to maintenance and stability. Every time an element changes in a web application, a test recorder requires recreating the correct test from scratch. Similarly, if a test breaks, you would have to re-record it and let it run all over again. This causes many testers to complain about “record and playback” tools because it can turn into a technical debt trap.
On the other hand, there are codeless automated testing tools that do test modeling instead of a recording. On the surface, it functions just like a recording in terms of its simplicity. Just like a recorder, you can point and click on real, online elements in the web application during runtime. Yet unlike a recording, it creates a model that you can apply to other parts of your software testing efforts.
Modeling for test automation is advantageous for three reasons. First, it makes test maintenance much easier. With modeling, you can take updated components of your test and simply drag and drop them into future test flows. There is no longer a need to focus time and resources on re-recordings. Second, if you make a change in a part of your test model, the change will automatically reapply it to other existing models that use it. Third, if a test model breaks due to changes in the application, testers can fix the problem right then and there by rebinding the elements.
AI and ML Capabilities
Artificial Intelligence and Machine Learning have impacted many industries over the years, and test automation is no exception. For software testing, in particular, AI is useful for identifying the right elements in a test after they undergo changes.
If you’re conducting Selenium test automation for web applications, you know that each web page has many elements. Some notable examples include a search bar, company logo, and buttons to click to other pages on the site. Each element is also made up of multiple attributes, such as ID, CSS path, position, size, and value.
Machine learning can be used in a variety of ways during the software testing process. One way it can help in test automation, in particular, is also by reducing the time spent on test maintenance.
When it comes to test maintenance, machine learning provides an innovative way to answer the following question: “Which element am I looking for when running a test?” By taking into account all of the attributes of an element and allowing you to assign different strengths to each one, ML allows your test flow to update automatically. This allows for more stable and resilient tests, which require little to no maintenance.
When considering a codeless automation testing tool that has ML capabilities, there are two factors to look out for: its accuracy rate and misclassification rate. With a high accuracy rate, your tool should be able to identify your testing elements correctly. With a low misclassification rate, the tool should produce minimal false positives or negatives. These are two stats you should ask for when evaluating this type of solution for your company.
Visual Test Creation
With codeless test automation, testers put a lot of emphasis on the tool’s visual components and overall UI. If the people using the tool cannot create test scenarios quickly or easily, it defeats the purpose of using it in the first place. They might as well invest the time to learn how to code within a test automation framework like Selenium.
Your codeless testing tool should be as visual as possible, where both manual testers and developers can clearly see which elements they are testing. This will allow for stronger collaboration between these teams, as well as an increased ability to drill down into each element and ensure that the tests are set up and functioning properly. Plus, it makes it easier to reuse important elements in your tests for new test flows.
Another reason why the visual component is so important to a codeless test automation tool is that it gives you the option to create tests before the actual code has been written. Working on a virtual canvas with an intuitive UI, you can design tests in an abstract way based solely on the product flow or product wireframe. After the development team has coded the feature, you can get the specific details of each flow and bind the actual elements retroactively during runtime. This has a strong shift-left benefit for companies that pursue continuous testing to achieve an agile work process.
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