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Software testing is currently undergoing a big transformation. We are seeing a major move towards using Artificial Intelligence (AI) to automate the design and validation of tests while significantly reducing reliance on human effort. With AI in the mix, banks and financial organizations can generate and execute tests autonomously, refining its capabilities through human guidance and input. As a result, we are on the brink of having access to virtual testing workforces for every development team.

In the ever-evolving world of automation, a groundbreaking concept has emerged: composable automation. Despite its recent emergence, composable automation holds tremendous potential to revolutionize the testing process, enabling testers to enhance efficiency and maximize reusability. In this article, we will delve into the essence of composable automation, with a specific focus on its application in test automation. Additionally, we will explore the multitude of benefits it offers, paving the way for a more streamlined and agile testing approach.

In the realm of AI testing, the concept of explainability holds immense importance. While the capability of AI in making decisions and reshaping how organizations operate by revolutionizing processes free from the errors and prejudices of human workers, the main prerequisite for its success is being able to understand and trust those decisions. That means there is a need in all industries for transparency, interpretability and explainability AI in order to avoid a future built on flawed and exclusive insights.

The banking industry has seen significant growth in the demand for test automation in 2022, driven by the need to support digital transformation. Going into 2023, banks continue to seek new ways to improve their efficiency and resilience. In this context, automating software testing will become increasingly important to achieving these goals.

According to Gartner by the end of 2024, 75% of organizations will shift from piloting to operationalizing AI. It may sounds like a risky plan for companies to adopt but in reality they translate technology advantage into competitive advantage within their industry. A plan that goes beyond post-COVID-19 world and can overcome unpredictable market shifts requires an ever-increasing velocity and scale of analysis in terms of processing and access. Embracing technological innovation is no longer just an option. Customer expectations and demand for new digital services are already increasing at a rapid pace.

Artificial Intelligence augments testing to be zero-touch and autonomous

While DevOps and “continuous everywhere” transformation requires the ability to assess risks with every “sprint”, with speed and quality, testing on the other side has become the bottleneck to taking full advantage of more Agile methods to software development.

Traditionally requirements have been managed in flat requirements documents or management software which represent requirements as text. This makes it hard to have a holistic view of the requirements and their associations, and see the size, scale and complexity of a project. In most cases the result delivered often looked very different from what the business had envisioned.

This is the third part of our blog series on the challenges of traditional test automation.

Continuing our blog series on the challenges of traditional test automation, we come to realise that the applications we were test ten or twenty years ago are not the same anymore.