Artificial intelligence in the world of software testing

Artificial intelligence in the world of software testing

As a relatively new and ever-growing technology, the definition of ‘Artificial Intelligence’ (AI) is constantly evolving.

While various analysts and experts across the globe have come up with their own interpretations, the general definition is that AI learns using data, using a ‘human-like’ ability to understand environments and contexts.

Although currently used in a variety of sectors, from analysing facial expressions to processing natural language, AI has become more of a ‘buzzword’ in the news in recent years. Scaremongering about ‘robots taking our jobs’ is common, especially referring to industries where manual, repetitive tasks are essential components.

Despite this scepticism, software and test engineers have always wanted to automate everything, and there are some genuine benefits to be gained from the technology.

So, how can businesses leverage the power of AI in their software testing, and what place do humans have in this process?

“Test smarter, not harder” to meet challenges

In the world of software, the testing stage is a critical task. However, it is also often impractical, time-consuming and expensive, with maintenance forming one of the biggest hidden costs in test automation. Automation within this industry, however, would help overcome this and allow testers to produce their best work and, thanks to today’s advancements in technology, it is now possible, in most cases, to hand over the task of test design and validation to AI.

An ‘AI approach’ to checking for quality thrives on the very things that cause headaches for hand-crafted testing. By combining machine learning (inputs and outputs) with analytics (behaviours) to facilitate decision making, you have the power to unlock the patterns in this data, drive automation and improve testing efficiencies.

The use of AI in QA

With the help of AI, QA teams can train systems to go through application log files and identify the hotspots. Implementing this technology in software testing will also help to improve test planning and test coverage of systems.

Other areas where AI can be leveraged in software testing includes identifying behavioural patterns in application testing, analysis data from social media, defect analysis, estimation and efficiency analysis, non functional analytics and machine learning test programmes to generate test data.

While there are still question marks over the use of AI in broader applications, within the software testing industry the technology can automate the manual, time-consuming processes and activities in order to save time, money and resources, with software testers able to focus their attentions on what they do best; making good software.

Pooja Tyagi, testing engineering specialist at NTT DATA UK

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