Test Automation In the World of AI and ML

In the realm of software testing, technology has always played a significant role. From the first computers to today’s most advanced test automation tools, it is safe to say that technology has simplified our lives. Artificial Intelligence (AI) and Machine Learning (ML) are the hottest buzzwords in the software industry today. The Testing community, Service-organisations, and Testing Tools companies have jumped on this trend. Furthermore, AI and ML are being used by many businesses to undertake test automation operations since it improves quality testing results in considerable time and cost savings. This post looks at test automation in the world of AI and ML.

What is Test Automation?

Automation Testing is the process of measuring and ensuring that a software application performs as designed. The main advantages of automation testing are improving the overall performance, reducing development costs and preventing bugs.

Why do we need Automation?

As you all know, time and accuracy is the primary concern in today’s world. To overcome this, you need to opt for Automation. Below are a few reasons for adapting automation technology into business:

  • Automation saves a lot of time, and the output is almost accurate.
  • You can also check if it meets the functional requirement before introducing it for production.
  • It also minimises the manual efforts into a set of scripts.
  • Results can be checked with previous test cases any number of times.
  • If the software is tested manually, chances are high of making mistakes and skipping the lines of codes. 
  • The most crucial aspect of Automation is that you need to know what needs to be automated. The result of automated tests should be captured and always be the same; this is called Test Stability.

Info 

How is Automation Testing helpful?

The rapidly expanding field of AI and ML will provide more business benefits to more industries than most people today can imagine. But its effects on testing, especially as it pertains to test Automation, will be significant. In other words, these technologies will present challenges for testers, but if we are proactive in our approach, we can also benefit from their advances.

One can automate testing by tools like Selenium, which can automate test cases across many web browsers in programming languages like Python and Java. Automation testing is useful:

  •   When applied on different hardware and software platforms,
  •   Multiple datasets
  •   Time-consuming projects
  •   To eliminate human error
  •   Repetitive functionality tests
  •   Complex projects

 

What role do AI and ML play in Test Automation?

Artificial Intelligence(AI) and Machine Learning(ML) are emerging continuously as the hottest topic in technology industries. Automation is possible with AI and ML. Test tools like Selenium or mobile test automation tools can use ML to parse user interface elements, create data-driven test theories, and even execute long-running tests. However, there are tradeoffs associated with testing with AI and methods that leverage AI. The best option is to combine the strengths of automated automation testing tools with those of AI and ML. 

AI and ML will enhance automation testing in two ways: 

  • First, they provide data that the tests can use to refine solutions; 
  • Second, they provide real-time updates on data as it changes over time so that the tests can keep pace within milliseconds.

Selenium is one of the powerful tools used in data science. With Selenium in python, you can gather and stock MySQL, CSV files etc. “Codeless Functional Test Automation” is another trendy topic right now, where the machines are allowed to evaluate the software product. AutoML is the word used to describe a set of tools and libraries, and the typical model selection process is automated using these tools and libraries. The establishments extensively recognize AutoML to get the best result out of a set of data. It is now an essential part of any data science and AI project. 

Info

Conclusion:

Testing and test automation are not going away. With the advent of AI and ML, many end-to-end tests will have to be automated. In today’s reality, one needs to develop and test the software beyond just the functional scenarios. Test automation is nothing to fear if it is targeted at providing the right set of tests, and these are kept separate from other types of tests where they can often create problems. Examining test automation in the world of AI and ML requires a some deep thinking. This post only skirts the surface. 

The AI market will continue to grow, allowing businesses to automate many more processes than ever before. We’ll see an increase in the scope of test automation as AI becomes incorporated into the testing software. And with that, we’ll see the ML component of ML testing having a profound impact on how businesses choose to implement their test automation moving forward.

Check out all the software testing webinars and eBooks here on EuroSTARHuddle.com


Related Content


About the Author

Phurba

I'm Phurba Sherpa, a passionate blogger who loves writing about the latest technologies. I also write educational and technical content regarding data science courses, Artificial Intelligence (AI), and ML. I've always believed in smart learning processes that help readers to understand concepts, and writing is one of the ways. I always prefer articles that will encourage tech enthusiasts in growing their careers.
Find out more about @phurba1234

One Response to “Test Automation In the World of AI and ML”

  1. Thank you Phurba! Like you state, the post only skirts the surface of the matter – by the way to which one could change the Selenium and code based approach into being ‘Intelligent No-Code Quality Automation’. Already today, automation tool AI/ML technology enablers can automate the entire Software Development LifeCycle while including Process Mining, Test Automation, and RPA. There real (production) users contribute with snapshots of their behavior/navigation or do up to full code coverage test scope application blueprint baselining – used by ML to determine changes and self-heal or even self-automate Test Automation. Yet another dimension can take automated API tests, derive UI tests, and use those for Performance Testing.
    Feel free to read more in my blogs for SogetiLabs: https://labs.sogeti.com/author/jesper-christensen

Leave a Reply