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For many companies, the mobile channel is either already their largest channel, or their fastest growing channel. Yet, somewhat surprisingly, 1 in 4 apps are abandoned after first use. And when you factor in customer (or “app”) acquisition costs, high abandonment affects not only your top line but also your bottom line, thus putting your business at risk. Mobile test automation will ensure an increase in app store ratings and decrease in negative reviews.
In today’s highly competitive mobile app market, delivering high-quality user experiences is critical for revenue and app store rankings. However, traditional manual and device-driven testing methods are often slow, flaky, and have poor ROI. To address these pain points, a new methodology called “Deviceless mobile testing” has emerged, leveraging AI to generate end-to-end API tests from the mobile app’s perspective.
Testing in a cloud and containerized world is often very difficult. One of the painful tasks that testers have to face is that once the containers are deployed, some of their APIs might remain accessible but others — the internal ones — are not reachable anymore. Even if a part of their APIs remains accessible, the requests sent from outside the application must fly through several management layers. Testers might want to remove these layers and send requests straight to the service they want to test.
Machine Learning and Artificial Intelligence (AI) are necessary means for test automation success. The ever-growing demands set upon QA and DevOps teams is overwhelming. AI and Machine Learning helps companies produce higher quality and quantity of work in shorter timeframes. It’s no longer a question of whether we should incorporate AI and Machine Learning (ML) into our testing process, but what’s the best way to do so, and through what channel.
API Testing can solve specific problems that trouble the world of test-automation. It is widely discussed that teams struggle with the following:
Flaky tests
Keeping up with the pace of development “in-sprint”
Maintainability
Functional Regression Coverage
Performance Testing
The testing industry is constantly changing, and every year brings new challenges and opportunities for teams to face. How are you optimizing your testing strategy in 2023?
Your teams are writing quality test automation, yet overall automation rates have struggled to get beyond 15-20% of all tests executed [1]. To scale automated testing and achieve sufficient in-sprint coverage, an alternative is needed to manual test creation, brittle test maintenance, and working in a skilled silo that finds bugs only once it’s too late.
With an ever-increasing need for high quality releases and feature rich digital apps, the demand for faster app development life cycle has become a non-negotiable. Today the world is racing against time to get the best of out of their apps has become the key differentiator in the competitive app market. While implementation of various testing methodologies like DevOps, Continuous Integration and Delivery have helped accelerate the process, there is still a lot of scope for improvement. Of late Artificial Intelligence based Augmented Testing has taken center stage to show us ways to enhance and accelerate our efforts.