Codeless test automation has become increasingly popular in recent years, as it allows non-technical testers to create and run tests without the need for programming knowledge. When we look at where we have reached in terms of advances, we see how much codeless test automation has evolved. The evolution of codeless test automation has been driven by advances in technology and changes in the software development process.
Let us take a look at how the test automation process has evolved over time.
## Evolution of Codeless Test Automation
Manual Testing: The traditional approach involves manual testing where the testers are required to write codes to create test scripts. This approach required testers to have programming knowledge, which limited the number of people who could create and run tests. In addition, the scripts were often brittle, meaning they would break easily when the application being tested changed.
First generation codeless test automation tools: The first generation of codeless test automation tools aimed to address these issues by providing a graphical user interface (GUI) for creating test scripts. These tools allowed testers to generate test cases by dragging and dropping elements from the application being tested onto the test script. While these tools made it easier for non-technical testers to create tests, they were often limited in their capabilities and lacked flexibility. All code-based test automation tools fall under the first generation codeless test automation tools category.
Second generation of codeless test automation tools: The second generation of codeless test automation tools which emerged in the mid-2000s, focused on making the creation and maintenance of test scripts even easier. These tools reduced the burden of writing the test scripts as they were automatically generated; hence, reducing the dependency on the programmers. All codeless test automation tools fall under this category.
AI/ ML testing: The latest generation of codeless test automation tools, which are still evolving, use artificial intelligence (AI) and machine learning (ML) to automate the creation and maintenance of test scripts. These tools use algorithms to learn how an application works and can generate test cases automatically. They can also update test scripts automatically when changes are made to the application being tested.
## Which one is better?
AI and ML-powered codeless test automation tools have several advantages over previous generations. First, they are much easier to use than previous tools, as they require little to no technical knowledge. Second, they can create and maintain tests much faster than human testers, which is particularly useful in Agile and DevOps environments where the pace of development is fast. Third, they can generate more comprehensive test coverage than human testers, as they can test all possible scenarios and edge cases.
Conclusion
Codeless test automation has come a long way since its inception. The latest generation of codeless test automation tools, powered by AI and ML, have the potential to revolutionize the way we test software. While there are some potential drawbacks to consider, the benefits of codeless test automation are significant, particularly in Agile and DevOps environments where speed and efficiency are critical. As technology continues to evolve, we can expect to see even more advancements in codeless test automation in the years to come.
Top comments (0)