Having a reliable QA strategy is important for the smooth functioning of the software. While there are software testing methods that utilize the latest technological advancements to pinpoint bugs, save time, and run various test scenarios, adding Artificial Intelligence (AI) and Machine Learning (ML) to the QA process takes it to a next level enabling businesses to enhance their testing process.
Types of Test Automation Using Machine Learning
- Automated User Interface Testing (UI)
It can be fascinating to manually test website visuals, but it can also be challenging to catch some broken elements on the page. In this case, Machine Learning is best used to detect and verify UI bugs using image recognition technology.
- Developing unit tests
Machine Learning helps developers spend more time writing code and less time creating and running unit tests. The creation and maintenance of AI-based unit test scripts are also useful at the end of the product life cycle.
- API Testing
API testing is quite challenging without Machine Learning or Artificial Intelligence since it involves understanding how APIs work and creating test cases and scenarios. Machine Learning in test automation allows you to analyze and create tests based on API activity and traffic. However, modifying the tests will require an understanding of Representational State Transfer calls and their parameters.
- Testing Scripts
Any update, upgrade, or code change will require altering the test scripts; therefore, you must qualify several test scripts. Artificial Intelligence and Machine Learning tools can predict whether a test application will need multiple tests. As a result, you avoid wasting your time and money on unproductive test cases.
- Artificial Intelligence and Machine learning-based test data generation
Datasets are the building blocks of AI models. The same is true for test scripts. As part of test automation, Machine Learning can be used to generate data sets resembling personal profiles such as photographs and weight information. The information is derived from trained Machine Learning models that utilize existing production datasets. In this way, datasets are created comparable to production data, which is ideal for software testing.
- Regression Testing with Robotic Process Automation (RPA)
RPA automates existing IT systems while maintaining them simultaneously. A scan of the screen, navigation of the systems, and identification and collection of data occur. Through web and mobile applications, all tasks are automated and run through robots. In addition, its main advantages are scalability, cost savings, improved productivity, codeless testing, and accurate output.
Machine Learning and Test Automation: Future Applications and Opportunities
Artificial Intelligence and Machine Learning are just scratching the surface for test automation. These technologies are still in development and have huge potential that could significantly enhance current test automation. There are lots of applications and opportunities that Machine Learning in test automation can provide in the future for IT enterprises, and here are some of them:
- Test automation will become the standard with Machine Learning, ultimately replacing manual testing. Despite this, companies will adopt a culture of frequent testing using test automation.
- With AI and Machine Learning taking over the role of producing, training, running, and generating results in less time and at a lower cost, IT organizations would be able to provide their customers with quality results.
- An AI-based test generation tool will solve the issue of too many or too few test cases. The intelligent tools will probably make life easier for testers and coders, whether they are testing UIs or APIs.
- In light of the rise of Predictive Test Selection, organizations struggling to manage huge datasets are likely to benefit greatly. Testing even a small change can take many IT enterprises hours and days. Test Selection predicts which tests will fail after incoming modifications are processed.
- Combining test automation and Machine Learning will save time and money now and in the future, encouraging companies to use this duo across all departments of their company.
