New trends for rapid and intelligent testing

[ Session 1 ]

Dr. Stuart Reid / STA Consulting CTO

Testing AI : A New Testing Specialism

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Artificial intelligence (AI) is now a mainstream technology that touches the lives of most of us. In some situations, such as image recognition, AI now provides better-than-human performance and AI’s global market is predicted to reach US$500 billion within two years.

When asked, the majority of people believe that AI will profoundly change their daily lives in the next three to five years, however, their level of trust in AI companies does not match up with other companies.

This lack of trust may be due to previous well-publicised failures, concerns with ethics, or the lack of explainability that is inherent with AI. There is no denying that AI-based systems, and machine-learning (ML) systems in particular, are different from traditional IT systems, as the underlying code is not written by a programmer but is inferred from patterns in data.

This major revision of the classic software development paradigm has two significant knock-on effects. First, it has created the role of data scientist/AI specialist, now the fastest growing job category on LinkedIn. However, it has also created several new sources of defects, such as the training data, the ML model and the ML development framework.

This talk proposes a new software testing specialism that is focused on addressing the new challenges introduced by this paradigm shift to AI and ML. To be able to test a ML system, testers need to understand the underlying technology and how it can go wrong. They also need to be able to address those system attributes, such as ethics and trust, which are so important with AI-based systems.

With over 90% of companies reporting that they are increasing their investment in AI, many future consumers will be presented with a choice of AI products and system quality will be the main differentiator. Up until now, many companies developing AI-based systems have lacked serious competition and have followed an immature approach of either leaving testing to the data scientists or to testers who treat the system as a black box with little or no understanding of the underlying technology.

From now on, as AI-based systems grow in complexity and criticality, achieving acceptable levels of quality will only be possible by employing independent, AI test specialists. This talk will explain why they are needed, what specialist tasks they will do, and suggest how we should handle the transition to a future where the AI test specialist is a recognized role.

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[ Session 2 ]

 Mette Bruhn-Pederson / Safe Journey P/S CEO

What is Agile Testing at Scale?

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Agile software development methodologies are now "old news". However, there has been a resurgence of interest in Agile where it is no longer about development teams being agile, but about the whole organisation and business being agile. Several new frameworks have emerged in the last decade that address more agile teams and the whole organization. What these frameworks have in common is that they do not provide much input on how we handle testing and quality assurance at an organisational and strategic level. In this presentation, Mette Bruhn-Pedersen focuses on the typical challenges and how we can rethink the concept of "quality assistance" so that it is not only about the teams that develop and maintain systems but the whole organisation. Furthermore, Mette will explain what the new Agile Test Leadership at Scale (ATLaS) certification is and why it is relevant for you as a test engineer and especially test leader in 2022.