In recent years, artificial intelligence (AI) has emerged as a catalyst for innovation across various domains, including quality assurance (QA) in software. The use of AI in automated testing can not only accelerate processes but also enhance accuracy, test coverage, and error detection.
With the increasing complexity of applications and the frequency of development cycles, traditional testing methods often struggle to keep up. Manual testing is time-consuming, prone to human error, and does not always cover all the scenarios needed to ensure product quality. While testing every possible variation is utopian, costly, and rarely adds significant value, increasing test coverage through automation should be a key objective for quality teams.
Although test automation is already widely used to address these challenges, AI introduces a significant advantage. By incorporating machine learning algorithms and other AI techniques, automated testing gains the ability to learn, adapt, and improve over time.
There are various levels and approaches to applying AI during the automated testing process. AI can analyse software requirements and automatically create test cases, eliminating the need for manual scenario creation and ensuring broader and more precise test coverage. Machine learning algorithms can identify patterns in pre-existing test execution data and detect anomalies that may indicate potential software failures. Additionally, AI can execute multiple combinations of system usage flows, aiming to identify issues through exploratory testing.
Currently, many tools offer solutions for part or all of the process, such as testeRigor, Roost.ai, LambdaTest, Functionize, Perfecto Scriptless, among others. However, it is important to emphasise that not all companies or systems are ready for the proper use of AI, and not all AI solutions produce fully satisfactory results. Human instrumentation and review are still necessary—and likely will be for some time—because projects are not always properly documented, and there is often subjectivity in rules or scenarios involving complex integrations.
Therefore, the use of AI in test automation is fundamental for code review, coverage, and evolution. However, it should be used as a support tool for QA teams, not as a “silver bullet.
