Traditional approaches to test automation have proven ineffective in addressing the business requirements of rapid application delivery and faster time-to-market. The failure of traditional test automation often aligns with the automation coverage metrics observed in various banks and financial organizations. This underscores the critical need for teams to update their testing practices, with test coverage emerging as the biggest challenge they have to deal with.

The scalability of manual testing is very limited, as tests are generated one by one, making test expansion a linear process. However, the complexity can grow exponentially as new product features and states interact with existing ones. While testing may initially keep pace with feature development during the early stages of a project, the inconsistency in coverage versus functionality and complexity inevitably becomes apparent.

One of the most critical aspects of the current state of manual or traditional automated testing, is its limited scope of validation. When a new 'feature' is introduced, it will usually execute and pass, even if the new feature is faulty. Detecting these new issues typically requires over analytic human testing, but human testers often find themselves re-running basic tests that continue to pass. In doing so, they tend to overlook critical issues.

An AI-based quality assurance approach excels in areas that often pose challenges for traditional automation. Imagine an AI tool capable of navigating the app like an end user, recording every detail, including performance metrics and the precise locations of buttons and text boxes. This tool could rapidly generate and execute tens of thousands of test cases within minutes. Moreover, by providing the machine with a rich dataset of bugs and application data, it could suggest where the app team should prioritize their efforts. Furthermore, when such an AI tool tests hundreds or thousands of apps, it continuously learns in the process, reaching the point where it will possess a wealth of testing experience to guide the test team in making informed decisions.

Arguably, one of the most critical advantages is that, when trained by experienced testers, such tools can outperform humans in many aspects of the testing process. AI has the ability to identify every change in the system, every new element introduced or removed from the application. It will examine all changes and assess their significance, leveraging the collective intelligence of the QA team, as well as insights from other teams that have categorized similar changes as either 'feature' or 'bug'.

However, some common reactions to the notion of an AI-based testing system include concerns such as 'I'll always be more intelligent than a bot!”,''How will the AI know what data to input into the app?' and 'How will these bots determine if the app is working correctly?'.

Perhaps the most challenging question is this: How can you be certain that your app is functioning correctly? The truth is, you can never be sure. You might have some tests, whether manual or automated, perhaps around 100 test scripts, and they may pass. However, these tests only cover a fraction of the potential states and scenarios within your app. The real value often lies in gathering feedback and bug reports from real-world users, addressing issues as they arise. In testing, what you truly want to know is, 'Is it working just as it did yesterday?' If not, are the changes beneficial or problematic? This is the essence of most testing, and AI bots excel at thoroughly examining thousands of aspects of your app, meticulously checking tens of thousands of elements to ensure it still functions in line with the previous version. With a swift review, a bot can confirm that 99% of your app remains unchanged, allowing you to concentrate on the 1% that has been altered.

Validata Sense.ai, our no-code cloud-native 'super-app,' redefines testing with its AI-powered features that accelerate digital transformation in the banking sector. By conducting tests with minimal human intervention or even autonomously, Validata Sense.ai streamlines the testing process, making it both faster and more cost-effective by reducing reliance on manual testers and associated resource-intensive expenses. It efficiently manages repetitive tasks, enhancing productivity to meet the demands of continuous delivery while improving quality and mitigating risks.

This AI-driven platform excels at processing extensive data volumes to identify defect trends and predict potential issues, swiftly correlating data to identify and resolve them. Validata Sense.ai provides proactive solutions by suggesting the most appropriate tests for the 'next best step' and optimizing user journeys to deliver an exceptional user experience. Additionally, it automatically updates tests in response to system changes.

Validata's AI technology is particularly well-suited for Regression testing, allowing for the comparison and identification of whether previously functioning application features remain operational. It automates test generation, leveraging advanced technologies such as natural language processing (NLP) and advanced modeling. This expedites the identification of defect root causes, enabling swift routing to the appropriate individuals or teams for resolution.