Robust Test Automation Framework using Artificial Intelligence

Robust Test Automation Framework using Artificial Intelligence

Testing software has become one of the most important phase of a software development life cycle since companies all over the world gain a competitive edge in the market depending upon the speed at which the testing is carried out. The faster the testing, the earlier the product is deployed and the earlier it reaches the customer.

A robust testing framework directly impacts:

  • The reliability factor, a company/ brand offers its customers
  • Reduces risks significantly
  • Reduces time taken in the cycle of product release
  • Improves go-to-market time
  • Improves overall efficiency
  • Code reusability

This indicates that building a robust testing infrastructure is integral to the overall success of the company. Upgrading to a fully automated testing environment, even more so.

Robust Test Automation, as the name suggests, deals with scenarios in which the software is most likely to fail. Testing on the two most important parameters – Safety & Reliability.

Now, coming to the framework side of things, a framework is something that deals with setting guidelines as to how to conduct a successful testing procedure. The better the framework the faster the testing process is, keeping the efficiency in mind, of course.

This is due to the fact that a robust framework contains the best practices that must be followed to maintain a stable, secure and reliable software product.

The basic types of test automation frameworks are:

  • Linear Automation Framework

A super-fast way to generate test scripts and due to its record and playback feature.

  • Modular Based Testing Framework

Testing process can be modified as it is conducted in modules and creating test cases is easier as you can reuse old ones

  • Library Architecture Framework

Very closely related to the modular approach, however, it has a higher affinity for reusability as there exists a library of functions rather than just modules.

  • Data-driven Framework

A convenient way to test multiple scenarios and handle multiple data sets, hence, making for a faster test process.

  • Keyword-driven Framework

A modified version of the data-driven framework, where each keyword requires a written code in order for the software program to run, however, the code is reusable and can run across a number of test scripts.

  • Hybrid Test Automation Framework

A comprehensive framework, that contains all the above-mentioned frameworks integrated into one platform. The tester can mix and match according to their need and necessity. It is highly adaptable.

Recently, with the introduction of AI and ML into the testing field, there has been a new type of testing known as Hyper- Automation Testing.

This is basically an advanced level of test automation powered by AI/ML where frameworks are created by logical reasoning and problem-solving methodologies, wherein they train the software to identify and act upon the input data, without any interference of humans.

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