Last updated on April 8th, 2024 at 06:23 am
As a software tester, I have experience of employing Artificial Intelligence (AI) algorithms in my testing procedures. When I tell this to a friend of mine from outside of the IT industry, they are surprised. Siri, the voice-powered personal assistant or Tesla, the electric car are the kind of AI applications the general public identifies AI with. However, AI has had applications in versatile industries, including the software testing field in the last few years.
Artificial intelligence does not have a clear definition. The simplest way to describe it would be as the intelligence displayed by machines instead of the natural intelligence of humans and other living organisms. An AI software testing company is trained to learn, reason, and self-correct itself. AI relies heavily on Machine Learning (ML) by which machines can act without being explicitly programmed.
Here are 7 Benefits I Found in AI Testing over Traditional Testing:
1. Fast-paced testing
Manual testing is slow, taking a toll on resources and costs. Code changes require new tests. Regression testing cycles drain quality assurance agents. AI automates test processes, enabling precise and continuous testing, at a much fast pace. Test automation simulates thousands of virtual users, which would not be possible in controlled application testing. Moreover, AI tools tell with precision the number of tests required to test the changed code.
2. Better defect tracking
In traditional testing methods, bugs remain unnoticed for a long. These ignored bugs become a nuisance later. AI can catch defects in seconds. AI analyses these errors. As test data grows, so does the number of bugs. AI automates processes, so codes are auto-corrected and bug tracking is assigned to QA teams. AI takes fingerprints of failures on debugging logs and identifies duplicate defects.
3. Script Automation
With AI, there is no need for automating a test script, as it is automatically executed by the AI algorithm. AI sorts through log files. It makes test cases more stable so that they are not brittle when locators are changed. With AI, test scripts have self-learning capabilities. AI can learn the page load behavior and get mock responses from the server.
