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AI Automation and Testing

By Sakshi Pandey, Community Contributor -

Artificial intelligence is a field that, in its most basic form, combines computer science and substantial datasets to facilitate problem-solving. There are three primary divisions of AI technologies:

  1. ANI (Artificial Narrow Intelligence) – This falls under weak AI, a type of AI trained to perform specific tasks. Most AI we see today is ANI since it narrowly focuses on solving a specific problem.
  2. AGI (Artificial General Intelligence) –  This falls under strong AI and is a form of AI that remains theoretical. AGI has not yet been achieved but is projected to be a machine with problem-solving, learning, and other intelligent traits equivalent to a human being. 
  3. ASI (Artificial Super Intelligence) – We have only been able to get glimpses of what such an AI may look like in sci-fi movies. ASI is another, as of now, theoretical AI. This is a form of AI that would supersede human ability and intelligence.

Recently, ChatGPT has been making the waves as a state-of-the-art language model developed by OpenAI. Using natural language processing-based artificial intelligence allows for the AI automation of tasks involving human-like interactions, such as an AI chatbot.

The difference between AI, ML, and DL

Layers of AI

Although Artificial Intelligence, Machine Learning, and Deep Learning are all terms used interchangeably in most content, they are nuanced and different. As illustrated above, machine learning and deep learning are subsets of artificial intelligence, With deep learning being a sub-field of machine learning.

So what differentiates these fields

Artificial IntelligenceMachine LearningDeep Learning
The ability of a machine to develop its intelligence.The ability of a machine to use algorithms by which it can automatically learn from data and experience.The ability of a machine to learn via algorithms that mimic the human brain.
  • The simplest definition of the outermost layer, as discussed earlier, is a machine’s ability to use datasets to facilitate intelligent problem-solving.
  • The second layer, machine learning, is a subset of artificial intelligence. At its core, it uses algorithms to automatically learn from data and gain experience to solve a particular problem.
  • AI aims to build intelligent machines that can carry out tasks that would typically need human intelligence.
  • Machine learning became a subset of AI when people realized that teaching computers to learn to address and carry out tasks from datasets provided to them is more productive than rigidly programming them to execute specific actions.

The innermost layer, deep learning, is a layer that attempts to mimic the human brain using a concept called neural networks. One can think of these as akin to the neurons in the human brain. When these networks have more than three layers, the algorithm can be classified as deep learning. Deep learning differs from machine learning in how it learns. Non-deep AI and machine learning rely on human guidance to learn; The deep learning algorithms, however, are scalable and require far less human intervention.

AI Automation Testing

Artificial intelligence, Machine Learning, and Deep Learning are trends. With everyone chomping to jump on their competitors by employing AI, it’s no surprise that software testing companies are also getting in on the trend. 

General Applications of AI based Automation:

  1. Self-healing tests: Traditional test automation tools have specific identifiers to define the components of an application. When the application is updated or changed in any way, these components can also change. Due to the fixed definitions are given to these elements, the tests functioning fine before the change will now fail and provide a false negative result. Self-healing tests apply artificial intelligence algorithms to automatically identify unexpected errors due to dynamic properties and recommend a better alternative or automatically update the script. This testing stops tests from failing and saves the time a QA might have spent trying to find and fix the issue. 
  2. Visual Locators: In AI-based user interface testing, visual locators can now find elements, even when their locators have been altered, on a web application by vision. This eliminates the need for hard coding with Accessibility IDs or other locators. Furthermore, intelligent automation tools can now use OCR and other image recognition techniques to map the application, locate visual regressions, or verify elements. 
  3. AI Analytics of Test Automation Data: Tests generate copious amounts of data that must be combed to derive meaning. The application of AI to this process dramatically increases its efficiency. AI-powered algorithms can be used to detect and categorize errors. More sophisticated applications of AI algorithms can also identify false negatives and true positives in test cases. This can be very helpful and significantly reduce the workload of QAs. 

A Case Study to Explore the Potential of AI: Spidering Web Applications with AI

The automation framework Selenium was initially designed to test web applications. However, one of the more diverse uses of Selenium is web crawling, also known as spidering.  While faster tools for spidering, such as the scrappy library in python, are available, Selenium is best for more complex applications. 

spidering
Selenium can be used to spider an application and collect relevant data such as screenshots and download the HTML of the application with beautiful soup. It can then learn how the web application should look and function from the data collected. An unsupervised learning technique such as anomaly detection could be applied here to find anomalous elements or visual regressions. Supervised learning can also be employed by providing screenshots and HTML data with labels to a classification algorithm.

Once the algorithm can successfully identify issues in a test run with the web application. It can be repeatedly deployed on the same web application to identify changes or irregularities, flagging them as possible bugs for further manual review.

The Future of AI Machine Automation

Artificial intelligence is a field of endless possibilities, and every year the use of AI algorithms to carry out intelligent test automation grows more sophisticated. Therefore, although a lot of the AI test automation tools are still in their infancy, it can be seen that there is great potential for AI to revolutionize the future of software testing.

BrowserStack allows users to perform automation testing on 3000+ devices, browsers, and operating systems. With Browserstack, users can test AI programs like chatbots and perform automated tests using different languages and frameworks.

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