Artificial Intelligence Driven Testing is a recently introduced ‘new-age’ method of testing software that takes automation testing a step further by enabling and leveraging the power of AI Tools and Machine Learning and inculcating them to get the best out of automation.

As much as Automated Testing is beneficial to companies and its advantages over manual testing is clear to see, however, automated testing too can be improved with the advent of AI tools into the software testing space, such as:

The areas of improvement in the automated testing space that are present currently are:

  • Test maintenance can be expensive since test suites are too fragile due to excessive changes in code.
  • Creating test cases that are unique, simple and transparent can be a challenge.
  • Testing efforts are usually duplicated in automated testing
  • Although automated testing has a higher test coverage compared to manual testing, it does not perform test coverage in a complete sense.

IGT’s AI powered automated testing solutions offers a host of benefits when apple to the automated testing method:

  • Better Accuracy
  • Improved Regression Tests
  • Enhanced Defect Tracing
  • Prognostic Analysis
  • Self Repair of Selenium Tests
  • Automated API Test Generation
  • Emerging and evolving Bots
  • Enables Visual Testing

AI Automation vs Automated Testing


While the end results and some benefits of AI powered Automation and Automated Testing may coincide, their difference lies in the fact that automation mainly deals with automated laborious humans, where the output can be determined, while AI mimics human intelligence and has the ability to predict and correct itself by interpreting data that has been collected.

Adding to that, AI powered automation also involves a different testing life cycle as compared to manual and automated testing.

The steps involved in AI Automation testing life cycle are broadly classified into the TEACHING phase and the TESTING phase.

The teaching phase involves planning, designing and preparing test data that has to be taught to the AI.

The testing phase involves executing the test and test closure where the results are further studied and a new cycle begins with the new data that has been found.