As we all know QA testing and software development go hand in hand. The rapid evolution of software development has forced quick advances in the testing field, we can see this in the quantity of testing tools techniques, and processes that exist today.
Quality assurance helps a company create products and services that meet the needs, expectations, and requirements of customers. It yields high-quality product offerings that build trust and loyalty with customers. The standards and procedures defined by a quality assurance program help prevent product defects before they arise.
In simple words, QA is a systematic process of determining whether a product or service meets specified requirements.
Artificial intelligence is the simulation of human intelligence processes by machines, this means creating algorithms to analyse, order, and draw predictions from data, also, it involves acting on data and learning from new data which means improving over time, just like a human, and that’s our goal here, using this ability to learn and implement into the QA process.
Here’s a chart showing the growth of AI through revenue and why we should start thinking about this as a possibility.
Machine learning is actually a branch of AI and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions. These insights are what drive decision-making within applications and businesses, ideally impacting growth.
Before discussing AI automation let us understand the role of AI in software testing and the benefits of having such a tool. Over time, the software development domain has grown a lot, whether in development or delivery. Also, the rest of the software development process has undergone a significant transformation, adopting a new DevOps culture that prioritises concepts that promote continuous delivery and that’s why we have test automation.
Now, even the general test automation is also changing with the approach of AI in the domain. If one wants to succeed in any test automation, the right tool with the right technology is essential.
Therefore, the most significant advantage that AI-Powered automation provides compared to traditional tools like Selenium is maintenance.
The ability to adapt to changes in the app through AI and generate new code each time to do that is what makes this concept unique and exciting, maintaining an automation suit with countless test scripts is not easy, and it’s time-consuming and all this because as we well know the application changes would be in constant change to follow the business requirements.
- More accurate
Everyone makes mistakes while performing QA testing especially manually, a machine will always successfully capture, record, and analyse precise data with greater efficiency.
- Saving time
Every time the source code is changed, repetitive work is involved, and an AI-based testing system could complete these tasks without a problem thus, software testing takes place more quickly.
- Better test cases
AI QA automation can help testers analyse the app by crawling through every screen while generating and executing test case scenarios for them, thus saving the planning time. It will also improve the quality of your test cases for automation testing . Artificial Intelligence will offer real test cases that are quick to operate and easy to regulate. The traditional method does not allow the developers to analyse additional possibilities for test cases. With the help of AI, project data analysis happens in a few seconds, and therefore it will enable the developers to figure out new approaches to test cases.
- Better regression tests
Regression testing is usually needed ASAP with progressive and rapid deployment.
Difficult regression tests can be carried out using artificial intelligence. Machine learning is a tool that can be used to write test scripts.
AI could also be used to validate changes that might otherwise be challenging to test manually.
- Predictive Analysis
AI automation in quality assurance can analyse and examine existing customers’ data to determine how users’ necessities and browsing practices advance. This permits testers, designers, and developers to be in front of developing users’ standards and offer better help quality. With ML, the platform consisting of AI improves with analysed user behaviour and gives progressively more exact forecasts.
It’s true that AI and ML are only as smart as the data and parameters they’ve been given, any type of application or situation where there is a need for a dynamic result is just not practical, so the more dynamic the scenario, the more difficult it becomes to implement AI and ML correctly and have them make the best decisions. Still, if they are used for what they’re best at they can detect and correct scenarios very quickly, it’s all about using the tools you have in a correct way, to sum things up, using AI makes a lot of sense as long as you use it for the right task.