From the introduction of the Internet to the widening of the mobile industry and the introduction of technologies like Artificial Intelligence, Machine Learning, and high-speed network solutions like 5G, things have never been the same.
Nevertheless, the initial focus of the development process and the technologies was only limited to converting every manual process to digital while enhancing the user touch with the technologies. However, I believe, the growing interactions of the users with the digital environment and the need for sustainable solutions have definitely become a reason for greater focus on quality and software testing.
In other words, when working on a digital product, the quality assurance and software testing process allows for determining the fate of an application in terms of end-user experience, reliability, performance, security, etc. And therefore, the entire idea of software testing evolved tremendously, allowing the introduction of concepts like shift left testing and test automation.
With that being said, it becomes necessary for testers, developers, business analysts, and other stakeholders to stay informed of how the future of software testing will likely change. And based on my personal experience and knowledge, here is how I see the future of software testing.
To underline, the future of software testing is more about existing and upcoming technologies and how they can be used to harness the maximum potential of QA processes. From refining the user experience to the integration of test automation with machine learning, IoT, and practices like Agile and DevOps, let us quickly jump on learning how they are likely to change the future of software testing.
Machine Learning (ML)
Right now, machine Learning is one of the most hyped concepts that are likely to change the quality assurance industry. With some remarkable changes made to the software and application development process, I can say, ML has all the potential to redefine the quality landscape.
According to Statista, the machine learning market is expected to grow from around 22.6 billion U.S. dollars to nearly 126 billion U.S. dollars by 2025, which likely increases the adoption of machine learning technology in the IT sector.
To underline, machine learning in software testing can be used to run code-specific tests with test suite optimization. Besides, Machine Learning could be used by testers to work on predictable test configurations and can run those tests automatically.
Above all, integrating machine learning solutions into a test process could simplify the identification of high-risk application states, simplifying the ranking of the regression tests.
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