If you research AI in mobile app test automation, you will find plenty of content featuring how transformative AI is in the field of testing. However, some exaggerations are running around that pose the question – is AI mobile test automation a fact or fiction?
As testing systems and scripts evolve, the idea of AI-driven mobile application testing is appealing. One of the biggest roadblocks most organizations face is the burden of maintaining automated testing scripts. Plenty of tools out there claim to offer AI-driven mobile app testing solutions. But before diving into any of these, it’s crucial to have a clear picture of AI in mobile test automation.
Mobile Testing And Why It Is Crucial
Mobile testing involves checking parameters such as consistency, usability, and functionality on mobile applications and testing that an app works as intended for users. Another main target is to ensure high accessibility to a broader target audience.
Due to the rising number of mobile users, the number of testing scenarios is higher. It results in higher complexity and gives rise to a severe need for considering a wide array of aspects while performing mobile application testing. Since almost everyone we come across holds a smartphone in their hand virtually all the time, it has become impossible to skip mobile testing.
Before starting with mobile testing, it’s essential to deeply understand the application components and their functioning on Android and iOS automation frameworks and device fragmentation. Since mobile application test automation still lies in the infancy stage to test flakiness.
Artificial Intelligence In Mobile Automation Testing: Benefits And Challenges
AI helps automate many repeated manual tasks that require intuition or judgment. It can use objective data to make crucial decisions regarding products and services that improve quality. Since AI replaces most human labor, you don’t need to hire more staff, which is an excellent cost-cutting solution.
AI in automatic testing improves accuracy by correctly performing each similar step with each execution. It also never fails to record detailed results. There are some situations in which QA tests an application with more than a thousand users that is next to impossible manually. Recreating plenty of virtual user sets that can easily interact with software, application, or a network is possible.
However, implementing AI in mobile app testing is not all rainbows and unicorns. The potential benefits are obvious, but it’s also important to acknowledge and address the potential challenges of using artificial intelligence in test automation. For example, the learning curve is steep, and training can take time if complexities in environmental changes are high with artificial intelligence. If you are on a budget, implementing AI might not be possible for you at the moment.
AI offers great help in creating test cases and automatically generating pseudocode and test scripts. AI also provides innovative insights such as wrong predictions, patterns of failure, defect hotspots, app stability, etc.
Ensuring software compatibility across different browsers, operating systems, and devices can be simplified with AI’s help in usability testing. As for security testing, AI keeps human errors at bay.
Artificial intelligence aims to completely transform the DevOps and Agile framework by helping teams identify, recognize, and deal with bugs and issues at earlier stages in the STLC.
Codeless mobile application testing is where AI significantly steps up its game. AI-driven scriptless or codeless test automation solutions have been making their way to the mainstream. When you are using AI, the algorithm eventually adapts to your app. With every test case being run, AI makes using your test case smarter.
Decision-making is an area that usually comes across as something only humans can do. But with AI automation making its way through mobile app testing, it can easily take over assignments that need decision-making and address complexities within seconds. However, we will still require human intervention with high-order tasks such as edge cases, security testing, usability testing, and test generation.
It is just a matter of time before we also manage to train AI on these tasks. Once we accomplish this goal, it will probably be a walk in the park for AI to take over them. According to the world quality report from 2020 – 21, 93% of the respondents have declared AI as one of the most critical progress areas in testing.
With wider varieties of browsers, operating systems, devices, etc., system complexity is ever-increasing. But release cycles have to be short to ensure a faster time to market. This makes it clear that mobile application test automation based on AI will play a vital role in providing high-quality products at optimum speed.
However, as we go back to the fact vs. fiction thing, AI won’t magically solve all quality assurance problems. The inability to have empathy towards end users is one of the most serious challenges of using AI in mobile test automation. All in all, expert guidance is necessary to bring the best out of AI.
Did you happen to hear something about Tesla’s self-driving cars? It’s all the rage right now, and the buzz is hard to ignore. AI in mobile test automation is equivalent in the field of testing. The hype is real, and it is hard to separate facts from fiction. Just like those cars promise to adapt to traffic conditions and roads, AI-based testing insights are also supposed to be adaptable.
Just like in any other space, Artificial Intelligence is making huge strides in software testing, especially automated testing. Therefore, there is no reason to get tangled up in whether it is a myth or reality. Consistency in implementation and patience go a long way. If you have any doubts regarding this relatively new subject, sound them off in the comments section below. We are happy to clear any queries as they arise.