Artificial intelligence (AI) has become vital to analyzing large quantities of non-structured data and identifying different patterns and relationships. Additionally, it optimizes processes across several domains, including voice recognition, autonomous vehicles, computer vision, healthcare, financial technology, and software testing.
What Does AI-Based Testing Consist Of?
Testing based on artificial intelligence can positively impact several facets of software testing, especially when it comes to test automation. Many different tools and frameworks have appeared to solve various problems related to test automation. Still, one issue that has always been a challenge is maintenance. One of the principal reasons for the latter is the use of static locators.
When utilizing static locators, you only use a single attribute of an element to identify it uniquely on the page; if this changes, the test is interrupted, and testers end up dedicating a significant amount of time to addressing the problem and fixing it. According to research, testers spend approximately 30% of their time on test maintenance. Imagine the cost of opportunity associated with this effort! A tester’s time is valuable. It is best spent exploring the application and providing information to help stakeholders make informed decisions about the product. AI-based testing can solve this problem by using dynamic locators.
The Tester’s Role in AI-Based Testing
AI can help write tests on its own and validate paths that were not previously considered. Another great advantage is that AI can execute tests based on different needs and perform maintenance tasks without interfering. It makes everything related to test automation smarter, quicker, and, above all, less tedious since repetitive and boring tasks can be managed with this technology. AI allows testers to focus on more important and valuable aspects.
With continuous development setting a more competitive rhythm combined with the pressure of automation inspired by AI (for example, robots or chatbots), a tester could be forgiven for wondering, are testing teams and quality assurance under siege? Are QA roles in danger of being eliminated or replaced, like in the manufacturing industry?
In the case of manual testers, they will not disappear. There will always be a need for manual management and this type of profile. Although things may be done differently in the future, manual testers will always be needed to design testing strategies. Manual testing and AI will coexist now and in the future, but they must grow when it comes to data management and understanding how machine learning works. In this way, testers can develop more focused strategies, guaranteeing software quality in each cycle.
How AI Is Shaping the Future of Testing
This article has discussed how AI will influence software testing and help solve some of the biggest challenges regarding test automation. In the future, the way testers will execute automated testing will change significantly in terms of adopting a risk-based approach to software testing.
AI can learn different user flows and create test cases based on real user data. This means testers will not need to spend time generating test data based on production users since AI will do this step automatically. This helps to increase test coverage and makes automated testing much more effective as it is created based on real user flows.
Users can register tests independently and use tests created automatically by AI to generate efficient automated test sets. In this manner, even people with no technical knowledge can augment software testing coverage.
Despite all the progress made in AI, the truth is that imitating the human brain is not an easy task. Ultimately, those who use applications and technological innovations are human beings; therefore, human understanding, creativity, and contextualization must be considered when ensuring a product’s quality. That being said, manual tests are still essential and should complement AI and automation. Although their roles are different, they should leverage each other according to their respective strengths instead of being compared.
- AI-based testing can positively impact several facets of software testing, especially when it comes to test automation.
- Dynamic locators reduce time spent on test maintenance, allowing testers to focus on more critical tasks.
- Manual testing and AI will coexist now and in the future, but they must grow when it comes to data management and understanding how machine learning works.
- AI can learn different user flows and create test cases based on real user data, increasing test coverage and making automated testing much more effective.
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