Test smarter and not harder – is a slogan of Software Testing and Quality Assurance. While complexity of application increases, it is fair enough to implement test strategy intelligently that reduces time to quality deliverables. Even though we use opensource tools and frameworks such as Selenium, Cucumber and few other licensed tools have their own limitations, when we think through that, it leads to an effective approach ‘Artificial Intelligence (AI)’- a smart and an efficient way.renders initially is
Artificial Intelligence (AI) Tools
Here are some of the popular AI-based test automation tools being used.
- Testim.io
- TensorFlow
- Appvance
- Test.ai
- Functionize
About Machine Learning (ML)
Machine Learning (ML) a subset of AI, is a pattern-recognition technology, it uses patterns identified by ML algorithms to predict future trends or outcomes. ML is powerful to find predictive patterns with large amount of complex information, and alerts on those differences.
Artificial Intelligence (AI) enhances Testing abilities
Artificial Intelligence (AI) is going to change testing in many ways in the coming days without replacing human. Keep reading, you will know other reasons.
UI Validation
Focus of most of the automation test engineers is to test functionality of application and these tests may ignore UI visual validation.
Using image-based testing tools, automated visual validation is still a limitation on color or size of elements. In such cases, testing activity ends up choosing manual verification which is error prone, where AI testing helps perfectly. You can use ML-based visual validation tools to find differences that human testers would most likely miss.
Regression Testing
Regression runs consume a lot of time at each staging test cycle in continuous integration and testing. Instead of running all common patterns for a small change in piece of code, ML lets choosing required set of precision tests to test the code. Moreover, AI tool lets report coverage of tests and risks within the application.
Write Tests And Prepare Data Sets
Spidering is a latest way of writing tests by ML, the newer tools need to be put through the web application automatically that starts crawling the application. As the tool is crawling, it also collects data by taking screen shots of features, downloads rendered HTML of every page, page load time measurements, and so forth. And it continues to run the same steps again and again.
Over the several cycles, it builds up a dataset and trained ML models for the expected patterns of your application.
API Testing
In the absence of UI, test automation is heavily dependent on backend testing through API calls, where ML Algorithms make it easier and faster to achieve quick release cycles.
DevOps And Report Analysis
During test runs, tool compares its current state to all the learnt/collected patterns, and if there is a deviation found, for example, a page that usually does not show JavaScript errors but it shows through test cycles or a script running slower than average, or a visual difference, then ML tool flags that as a potential issue. In some of these cases domain knowledge experts need to verify manually If the flagged issues are valid bugs. This approach of ML tools can reduce time to write test and track the areas of application to test thoroughly, and also the speed at which the application needs to be tested will be faster than the current world of Agile/DevOps based continuous testing.
Tests Recovery
Test automation engineers frequently face a challenge when developer keep changing element properties, that results into test failure. In these situations, ML tool decides from page object relationships and learnt patterns and the tool changes the locators automatically to identify the element.
Finally, AI Needs You.
Many automation engineers are needed not only with domain knowledge to train ML algorithm and test model development, but also who can analyse and understand algorithms, complex data structures and statistics. So, your job is safe. Stop worrying, upgrade AI knowledge requirements and do what you do best – happy test automation!
