The concept of artificial intelligence (AI) and the benefits and disadvantages of these technologies in a variety of contexts is a hot topic for many people and businesses, both in and outside of the technology industry. But what does it mean for people focusing on quality and testing in the application life cycle? Are they at risk of being made redundant?

AI impacts quality engineering (QE) in two primary ways; AI-augmented testing, where AI is used to improve delivery of testing, and testing of AI, where quality is ensured in the AI technologies used by business. We will be looking at the first one, AI-augmented testing.

By clarifying how AI-Augmented testing is defined, is currently used, and the ways these technologies are likely to impact the markets in the future, it should help with planning and adoption. Whilst many organisations have started utilising AI technologies in testing delivery, some are still hesitant to use them, or unaware of how they can best be used. This is an understandably prudent position to take, given that the AI technology space is still relatively immature.

Implementing nascent technologies, such as AI, because they are “new” and “cool” is seldom good practice. There are certainly use cases where organisations have demonstrated value and return on investment by implementing AI technologies but, for many, the barriers are still too high.

However, innovation is necessary to deliver value and more for less, and therefore taking prudent and well reasoned steps forward using AI is a good idea. Learning more on how AI can be used effectively and safely, instead of being fearful of the circulating uncertainty and doubt of AI, will help on the journey ahead.

Organisations are likely facing some of the following challenges in finding use cases for AI-augmented testing:

Excellent historical data and data quality is of paramount importance for these technologies to work correctly, as they need to be trained with data. Both AI and ML systems need to be fed the correct type and amount of data, or they cannot be effective. As an example, Tesla cars continuously collect data about surrounding environment, which is then used to enable autonomous driving features.

Correct, complete, and unbiased testing data, that is looked at both from a point in time and across a period of time, must be available or AI technologies will not be effective.

The vendor products available on the market are often focused on one aspect of the work, or a particular testing activity. It can make particular testing scenarios faster and more efficient, but it does not apply to all testing activities. Multiple products would need to be integrated into the lifecycle delivery toolchain for a more complete usage of AI in the testing lifecycle.

The skills needed to find and explore use cases, and build and maintain AI products are very different to traditional testing skills. The current testing team could be challenged to handle these technologies in-house. Teams would need additional skills such as data science, data statistics, machine learning, and knowledge of probabilistic techniques.

Despite these challenges, the usage of AI-augmented testing grows. There are now multiple testing tool vendors offering products that are AI-augmented, such as ACCELQ, Applitools, Appvance.ai, Functionize, mabl, Micro Focus, Sauce Labs, and Tricentis. This is a fast-moving market where it can be challenging to keep track of all the latest developed products or new vendors entering the space with innovative products.

The journey with AI-augmented testing has only begun and is still immature, but AI is a powerful technology that can disrupt markets. In the next 2-3 years, AI technologies are expected to be integrated across the entire testing life cycle and are likely to be an important part of quality and testing activities.

Keen to know more about what impact AI-augmented testing is expected to have on testing in the future and the most leverageable use cases? Then download our complete e-book for further insights into how this technology can be used to amplify the testing process.

About the author

As a Gartner analyst, Susanne owned the testing services Magic Quadrant for over 6 years, advising hundreds of CIOs, Application Leaders and Digital Transformation executives on software quality engineering related topics. At Planit, Susanne ensures our offering roadmap addresses current and future customer requirements, whilst incorporating emerging technologies and approaches.