The influence of Machine Learning in Quality Assurance

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Testing automation is often a weak point for engineering teams. However, implementing machine learning into your QA strategy can help turn this weakness into a strength.

Having established itself in many industries including transportation, finance and healthcare, machine learning is starting to break into the QA and testing world. It’s being used to ensure that quality assurance gives information on common errors, critical effects and malfunction patterns that could pose a risk to software stability.

How is it’s influencing the QA industry?

When it comes to QA, the machine learning strategy offers high accuracy and efficiency. It helps to detect redundant test cases, failed test cases and predicting and preventing coding errors.

Many companies have turned to machine learning to improve their testing process due to the ream of benefits that come with it:

Speeding up manual testing and improving the overall quality: With manual testing, each line of code can take anywhere between a week and a few months to produce. Even then, it has a higher chance of containing errors. ML is accelerating the process by speeding up test creation, reducing test maintenance and automatically generating tests.

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Helps detect bugs and reduces the probability of ignored bugs: While overlooked bugs isn’t the end of the world, it could have damaging effects on your company’s reputation. Thankfully, ML offers increased reliability when it comes to un-detected bugs as it learns from previous bug patterns in code and applies it to similar code patterns.

Helping to understand and forecast client requirements: Ensuring the client requirements are correct will allow testers to fully analyse customer data. ML offers an in-depth insight into more revenues at a reduced cost, enhanced user experience and competitive positioning in the industry as the company delivers a high-quality product. For example, Facebook has been using ML to find data predictions based on their user interactions with the platform.

Machine learning is looking to be the future of QA. For businesses, implementing ML into your testing strategy can not only make your life easier, but can also help to exceed your customer’s expectations and overall experience.

Upgrading your QA strategy can feel like a challenge – but it doesn’t have to be. If you’re looking for some guidance on how to successfully streamline your testing process by involving ML, we can help.

For information on how we can help, download our free guide here