Artificial Intelligence

How to leverage AI in software testing

The field of software testing has progressed from predominantly manual testing to incorporation of automation testing. The competitive market scenario and increasing technical complexity have encouraged that software development, testing, and deployment take place at breakneck speeds. The need to develop at a faster pace and test in a smarter way is increasing every day.

While the current practices of Agile, Continuous Testing and DevOps are keeping the software development process at pace, unlocking the true potential can only happen by leveraging the power of Artificial Intelligence in software testing.

The job of a software tester involves taking data-backed decisions to ensure the quality of software meets the set standards. Leveraging AI for the same task can result in software testing taking place at a much faster pace while managing a larger amount of data. Application of AI to testing enables the testers to go beyond the traditional mode of testing and adopt AI-enabled automation platforms instead.

The end goal of leveraging AI in testing to primarily reduce the testing lifecycle, making it shorter as well as smarter and augmenting the jobs of testers by equipping them with technology.

The existing AI landscapes

While it’s easy to imagine a dystopian future running akin to Terminator when you think of artificial intelligence, the reality is quite far away from Skynet. Artificial intelligence algorithms comprising of machine learning are already being employed for software testing.

AI is directly applicable to all aspects of testing including performance testing, exploratory testing, functional regression testing, identifying and resolving test failures and even performing usability testing. Here are a few applications of AI in the current scenario.

Testing of UI Interfaces

Image recognition, which is a major component of machine learning algorithms, is being used to take UI testing to the next level. The dynamic UI controls can be recognized, irrespective of their shape and size. AI can break down the user interface down to the pixel level to identify the image patterns.

Flexibility in Automated Testing

Simple changes in the application often result in test failures in Selenium or UFT because the testing scenarios focus on a singular path or selector resulting in considerable rigidity. AI/ML testing have the capability to observe and learn about relationships between various documentation segments. This provides flexibility to adapt to changes made in real time and since the scripts can automatically adjust to any changes, the tests become more maintainable and reliable.

Visual validation automation testing

Pattern recognition capability of AI makes use of machine learning to find out visual bugs in the software to ensure the visual validation of the application. Visual testing using AI is a QA method aimed at verifying that the user interface of the app appears right when viewed by the user and ensuring that the different components do not accidentally overlap with one another.

Using AI for visual validation ensures that even the errors which might be missed out by a human tester, are recognized at a significantly faster pace.

Writing test cases using AI

Spidering using AI uses machine learning to write test cases for the application by crawling through it and collecting the data. The data set thus built is then used to train the ML models about the application and what its expected pattern should ideally be so that with every new run, it can compare to the known parameters and issue a red flag in case any deviation from that pattern is detected.

Current AI and ML tools available

A number of companies are already operating in the field of AI-powered testing.

  • Applitools AI-powered visual testing and monitoring tool for mobile and web apps
  • Mabl Test automation, regression testing tool for codeless testing of web apps
  • AI AI-powered tool that uses ML for mobile app testing and documentation.
  • Perfecto Cloud-based mobile testing tool using AI analytics for reporting
  • Testim Machine Learning tool that authors, executes and maintains automated tests

Does a rise in AI testing signal replacement of human testers?

While we are just scraping the surface in terms of the capabilities Artificial Intelligence has to offer in the field of software development, it does not herald the extinction of the human testers. If anything, incorporation of AI in testing is simply going to make the life of testers a tad bit simpler.

The above-mentioned tools as well as the ones that will be developed in the future would only work towards making the testers more efficient, agile and would save them countless hours of time they would otherwise have to spend manually testing the applications.

Even with AI performing all the elementary tasks, the human expertise would always be required to oversee the testing process and skilled and specialized QA testers face no threat. Even when AI detects a deviation while performing testing, a human expert would still need to go in and validate the issues discovered.

Just as automation in testing is nowhere close to replacing the manual tester, same applies to AI as well. If you are a software tester, you probably shouldn’t start panicking about the possible takeover of your job by artificial intelligence. What you should do instead, is keeping yourself updated about the changing technology and keep automating.

1 Comment
  1. Mack patel 4 weeks ago
    Reply

    Nice post! Thanks for sharing this.

Leave a Comment

Your email address will not be published.

You may also like

Pin It on Pinterest