The Importance of Artificial Intelligence in Software Testing
1. Automating Visual Validation
Image-based testing, which makes use of automated visual validation tools (e.g. Applitools), is becoming more and more popular with every passing day. There are numerous machine learning-based visual validation tools available that can detect minor user interface anomalies that are likely to be missed by human eyes.
The primary goal of user interface testing is to ensure that each UI element is visually appealing, has the appropriate shape, colour, size, and position, and does not physically overlap with other UI elements on the screen. Even a simple ML test can detect and report on all of these visual bugs, eliminating the need for a tester to intervene.
2. Writing Test Cases in an automated fashion
The most significant application of machine learning and artificial intelligence in test automation has been in the automatic writing of test cases for software. Back in the day, we heard about web crawlers and “spidering,” which is the process of browsing a software/web in an automated and methodical manner using an automated script or programme, and which assisted us in locating 404 dead-end pages.
Now, artificial intelligence and machine learning tools have advanced significantly in their ability to learn the business usage scenarios of the application under test. All that is required is that they be directed to the software. While they are learning about the application, they automatically crawl it and collect useful information such as screenshots, HTML pages, and page loading time, among other things. Over time, they amass a sufficient amount of data from the application to be able to train the ML model to recognise patterns in the application.
When they are run/executed, the current state of the application is compared with the previously known or saved patterns (known as patterns of operation). If there is any error, visual difference, slow run time, or other similar issue, the system automatically flags it as a potential issue for further investigation. However, there may be instances where the differences are valid. In that case, the tester is responsible for validating the bug or problem.
3. Improved Reliability
Are you one of those who has had their UFT or Selenium tests fail because of minor changes to the application (such as renaming or resizing a field) made by the developers? If so, you are not alone. If you answered yes, don’t be concerned; this is a problem that most testers encounter.
Now, artificial intelligence can correct the code and make it more reliable and maintainable, eliminating the need to change the test every time a developer makes a minor change.
Artificial intelligence and machine learning tools can read the changes made to an application and determine the relationship between them. Changes in the application are observed by such self-healing scripts, which begin to learn the pattern of changes and can then identify a change at runtime without you having to do anything. In response to changes in the application, the ML scripts adapt automatically, reducing flakiness and fragility in the test automation.
4. Reduction in the amount of UI-based testing
Another change brought about by AI/ML in automation testing is the ability to automate without the use of a user interface. There are no exceptions when it comes to non-functional tests such as Unit Integration, performance, security, and vulnerability. In these layers, artificial intelligence and machine learning techniques can be used to generate tests. AI/ML applied to various application logs such as source code and production monitoring system logs, in addition to helping to develop bug prediction, early notification, self-healing, and auto scaling capabilities in the overall software eco-system, is another benefit of using AI/ML.
The use of artificial intelligence in testing lowers overall testing costs, errors, time, and scripting. Isn’t this exactly what we’ve been hoping for? There is no doubt that artificial intelligence and machine learning (AI and ML) are game changers in the software industry, and as a result, it will become a trend in the market in the near future. It is past time for software teams to adopt an artificial intelligence-based approach to software development, testing, and management.
References: https://www.digite.com/
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