AI & Machine Learning: Development and use of applications has been fully upgraded and affected by the benefits of AI. Machine Learning helps you achieve rigorous testing thanks to unique code checks, prediction of the main test configurations, log and defect analytics. The high level that Machine Learning reached in influencing testing optimization reflects in predictions that show the AI market accelerating to $200 billion by 2025.

Testing of Chatbots and Conversational Banking: Banks are increasingly start using chatbots and digital assistants for servicing their customers, making banking more human. Many believe that in the next five years, 50% of all banking interactions will be via voice-first devices. Just like testing any other software, chatbots should be tested. Ensuring quality from a usability and functionality standpoint is a must as it will also affect user experience.

Big Data: In order to ensure that performance and functionality of applications, you first have to evaluate and test the quality of a huge data amount that is generated at high speed. Eliminating the need to create new archives for each iteration by introducing new platforms that reproduce the behavior of individual layers of application data, you gain the following guarantees: efficient processing, constant import of new data in parallel with the processing of old and reducing application installation time. Considering the fact that the more data, the more time and more resources you need to run the test and achieve quality testing. Verification that each byte is successfully processed.

User Experience Test: The importance of testing user experience, especially in web development and mobile applications field, increases. A high quality UX that is user-friendly and allows a variety of users -whether simple users or software developers- to be able to use your application, is a main task for any quality product or device nowadays and the ultimate task for any company.

IoT Testing: A wide range of IoT devices with growing capabilities are available today creating a new dimension in methodical software testing. Despite the fact that 34% of the World Quality Report respondents said that their products have IoT functionality, they still don’t follow a counterpart testing strategy. To get synchronized with the new types of checks that are conducted in the IoT area, QA teams have to update their skills regarding usability, security, and IoT performance. Testers face a tremendous amount of work on monitoring communications protocols and operating systems and a multitude of possible combinations of all the elements involved.

DevOps: Robust, faster and better quality QA leads the industry right now. A tester is no longer “just a tester” as he is involved also in the beginning of the development cycle, collaborating with other departments. This results in a perfect final product despite the changes that might be made while building an app.

Testing Open Banking APIs: Large tech firms such as Google, Apple, Amazon, have opened the way towards implementing open API platforms, a model that a lot of banks are beginning to adapt as well. ‘Open Banking’ can offer a tremendous potential for banks to keep pace with innovation and create new revenue streams. Banks will need to test their APIs under real-world conditions, with real data, real-world characteristics and real performance requirements. Testing will need to happen in a Continuous Delivery model, where the developers’ changes are tested against every developer check-in.

Testing Digital Technologies: Testing for Digital Transformation will continue to be on the rise, as banks will not risk entering the market before testing their applications for the digitally enabled world. To achieve this, they will need to embrace flexible testing and development approaches such as Agile and DevOps.