Looking back, it is quite astonishing how software testing was and how it is today.
Earlier, after developing the software, it reached the testing stage. Then, the race began. The testing team raced to ensure that the software was bug-free.
Recently, a funny video was circulating on LinkedIn, which reminded me of the manual testing process. A software developer developed a coffee machine. He gave it to the tester. The software tester came. First, he took a cup and placed it in the right place, then touched the coffee button. The coffee filled the cup. The developer was happy. But that happiness did not last long.
Suddenly, the tester took another cup and moved it slightly to the right of the slot where the coffee came out. When he touched the coffee button, the coffee fell outside the cup and spilled. The developer was surprised. “What kind of testing is this?” he asked. Immediately, the tester said, “We think beyond normal usage scenarios. If the cup is not placed correctly, a warning should be given. Otherwise, the coffee will be wasted.”
Developers and testers think differently. When both perspectives come together, good software is created.
Later, automation testing tools came to make this testing easier. But having to write code to run the tests made it difficult for testers. They had to rely on developers to write the test code. Days passed. The tests broke because the developers changed the DOM selectors and the DOM order. The test script had to be changed again. Over time, maintaining these tests became a tedious task. As a result, teams would either abandon this approach or hire dedicated people just to maintain the tests.
In such situations, testers have even wondered whether they could write their own test cases and test without writing code.
Every application you use is tested thoroughly. Software Development Engineers in Testing write code to make sure that each submit button and login screen works without any issues. However, the product managers, business analysts, and customer support teams who know the product very well are often unable to participate in product testing because they have no background in coding.
This is a big issue that many people in the software industry overlook. The people who know the product best are not able to test it properly. Meanwhile, testing teams are struggling due to excessive workload and pressure to complete the work within deadlines.
This is where artificial intelligence brings meaningful change. Using generative AI, no-code, and AI-driven test automation platforms like testRigor helps everyone write tests in plain English and adapt tests even when the application’s interface changes. Instead of relying on fragile selectors, modern AI-powered tools understand the intent behind the test and make automation far more reliable and accessible than ever before.
Writing a Test the Way You’d Explain It to a Colleague
The main change that current AI-based codeless testing tools have introduced is support for plain language. You just need to explain what you want to test, and these tools will understand it and start testing immediately.
For example, let’s say you give the following instructions to an AI-powered no-code testing tool:
- Test a valid user login flow in an application using these credentials (username and password).
- Add the first product to the cart.
- Verify that the product price and the cart total are the same.
These plain English instructions can become a completely automated test. The tool interprets your instructions and automatically performs the required testing actions.
testRigor also works based on this concept. If you can write a sentence in English describing what you want to test, you can automate a test without writing code. testRigor analyzes your instructions, runs the test across multiple devices and browsers, and reports if issues arise.
Such tools are useful not only for non-coders but also for teams that want to move faster. When the acceptance criteria written by a project manager can directly become an automated test, the gap between “what is expected” and “what has been tested” is significantly reduced.
How Do AI-Based Software Testing Tools Work?
- Self-healing Selectors: When a button or another part of a website changes, the AI checks its surroundings, behavior, and appearance. Instead of waiting for someone to fix the test if it fails, the AI automatically updates the test to reflect the new change.
- Natural Language Processing (NLP) for Test Generation: AI can analyze plain language instructions and convert them into executable form. Some tools can even automatically generate test cases from user stories or acceptance criteria.
- Anomaly Detection: Beyond just checking if a button is present, AI systems can detect visual and behavioral changes on a website that are often missed by standard rule-based tests.
- Test Prioritization: AI can analyze changes in the code or previous failures and suggest which tests are more important. This helps the team focus on the important ones instead of running all the tests every time.
These are good engineering approaches. As a result, even non-technical people can now write tests that are more robust than those written by experienced automation engineers a few years ago.
What Is the Goal Behind AI-Based Software Testing?
In the software world, the term accessibility is commonly used to refer to how easy an application is to use for people with disabilities. For example, whether it is readable by screen readers, or whether the color contrast is accurate. However, accessibility is not often discussed in terms of who can participate in software testing.
In small companies and startups, quality assurance (QA) is something that can be done by anyone who is available at the time. In enterprises, the backlog of automation work can become a drag on development. In this case, having more people writing meaningful tests can help improve the reliability of the application and help it get released faster.
Also, domain knowledge is important. A manager who handles customer complaints knows exactly where a system is likely to break. A manual QA analyst, even if they don’t know coding, knows how a product works better than anyone else. Giving these people the right tools to use their knowledge and skills is about making the most of the expertise available.
Where This Is Heading
While current AI testing tools are impressive, researchers believe the future lies in autonomous testing agents. That is, instead of running instructions given by humans (even if they are in plain language), these systems will test the applications on their own, like a new user. They will create test cases themselves by clicking on links, entering data, and navigating unexpected paths.
Tools are being developed to fully monitor an app and automatically generate the basic tests it needs. Some are already available. Here, the human role is reduced from writing the tests to examining what the AI finds and deciding what is important.
This will bring about a major shift in the way teams work and the skills required. Rather than just someone who knows how to write test automation scripts, the importance will now be on those who can analyze the results provided by AI, identify real errors in them, and accurately communicate them to the engineering team. In short, the intensity of the job shifts from writing scripts to making thoughtful decisions.
A Realistic Precaution
AI-based testing will never replace the judgment or intelligence of an engineer. In systems with complex backend logic, performance requirements, and security standards, the services of experts who can understand the systems are still essential.
These tools are best used for extensive testing at the user interface (UI) level. They reduce the huge cost of regression testing. They also help involve non-engineers in the testing process. This allows expert testers to focus on the features that require real thinking, rather than wasting time on constant script maintenance.
For most teams, this is a huge improvement over the current state of software testing. While the wall of code hasn’t completely disappeared, for the first time, a side door has been opened in software testing for QA professionals who don’t code.
