AI in Software testing

AI in Software Testing helps to safeguard an application against potential application fail-overs.

Software testing is an important process that ensures customer satisfaction in the application. It is the planned way in test automation where an application is observed under specific conditions where the testers understand the threshold and the risks involved in the software implementation. AI in Software Testing helps to safeguard an application against potential application fail-overs which may turn out to be harmful to the application and the organization later on. As more and more Artificial Intelligence comes into our lives, the need for testing with Artificial intelligence is increasing.

In software testing, AI is a combination of cognitive automation, reasoning, machine learning, natural language processing, and analytics. Cognitive automation leverages various technological approaches like text analytics, semantic technology, data mining, natural language processing, and machine learning. For example, RPA (Robotic Process Automation) is one such connecting link between Cognitive Computing and AI.

Role of AI in Software Testing:

Improved Accuracy

Even the most experienced tester is bound to commit mistakes while doing repetitive manual software testing. This is where software automated testing helps by performing the same or repetitive steps correctly every time they are performed and never missing out on recording accurate results. The testers freed from repetitive manual tests have more time to create new automated software tests and deal with sophisticated features.

Automating Visual Validation

A method of testing that is getting more and more popular every day is image-based testing using automated visual validation tools. Many ML-based visual validation tools can detect minor UI anomalies that human eyes are likely to miss.

The main purpose of UI testing is to make sure that each UI element looks good with the right shape, color, size, and position and is not physically overlapping with other UI elements. All of these visual bugs can be checked by even a simple ML test without a tester having to intervene.

Helps both Developers and Testers

Shared automated tests can be used by the developers to catch problems quickly before sending them to the QA team. Tests can be run automatically whenever the source code changes, checked in, and notified the team or the developer if they fail. Features like these increase the confidence of developers and also save their time.

Reduced time

Manual testing is a slow process. And every code change requires new tests that consume the same amount of time as before. AI can be leveraged to automate the test processes. AI provides for precise and continuous testing at a fast pace. It can simulate thousands of tests in a short period. Thus, the time required for testing is reduced drastically.

Improving Reliability

AI/ ML tools can read the changes made to the application and understand the relationship between them. Such self-healing scripts observe changes in the application and start learning the pattern of changes and then can identify a change at runtime without you having to do anything. As the app evolves the ML scripts adjust automatically, reducing the flakiness and fragility of test automation.

Faster Time to Market

With software tests being repeated each time source code is changed, manually happening those tests can be not only time-consuming but also expensive. Interestingly, once created – automated tests can be executed over and over, with zero additional cost at a much quicker pace. Software testing period can be decreased from days to mere hours which translates directly into cost savings.