AI and Quality Assurance: The perfect exchange?

It is a well known fact that software development is a very time consuming activity, it requires the perfect combination of skills, logical thought and problem solving — hence why oftentimes other roles involved in the development of a software project can be overlooked, such as having a thorough and standardized testing process that allows for the creation of close to flawless products.

Add to the fact that enterprises usually look to develop software solutions to focus on other points such as agility, customer support and increased profits. QA can often fall behind in the thought process as a simple “testing” stage without actually having an in-depth discussion about how the Quality Assurance team can provide a lot of value to a final product.

At the end of the day, releasing something into the market that doesn’t work will eventually cause profit and even credibility loses that will affect the whole company. As time has progressed, technology has been able to supply innovation and solutions to almost every single issue out there, the same has happened with QA testing and engineering in general, mainly with the popularization of Artificial Intelligence solutions that can now be applied on testing processes.

Here’s what we consider are the main ways that AI can optimize and almost perfect the QA process for software development

AI provides more testing ground

Studies have shown that AI systems can perform certain tasks at a only a fraction of the time a human can, as shown in this article from Duke University on an automated process to track active neurons, which means that they are able to utilize time in a more productive manner thus leaving more open room for testing opportunity. AI excels in performing critical parts of the testing process such as regression testing which is known to be time consuming and due to their repetitive nature can result in having certain errors or processes being overlooked, by allowing an AI system to perform regressions its possible to utilize their algorithms to better forecast tests and provide estimates on possible outcomes, this process will enable the development of even more test cases and will result in a very fulfilling testing process that grants the possibility of releasing error free software.

Resources can be better utilized

A big issue in all major agencies is that resources are often either under allocated or over allocated, this can cause a major gray area that affects both productivity and profits by leaving tasks unattended and no opportunity to look into more ways to upgrade workflows and create new ideas. Leaving AI systems to perform the bigger and more automation-friendly chunks of the tasks during the day allows human resources to focus on tasks that require more analytical thought, research and are an overall mix of data analysis and objective thinking.

Damage control will be more effective

Even after trusting an automated system to test a software product thoroughly, there’s still the possibility that something can go wrong after launch, this is where we can trust an AI to identify incoming feedback based on the product that was released, whether it be by tracking google searches, online reviews or evaluating site recordings to study user behavior. Contrary to human resources, AI systems can be active 24 hours a day collecting outpouring information and delivering real time reports that will provide stakeholders relevant information for a better decision making process on how to control and solve outstanding issues that can compromise user experience and unanimous interests.

The biggest takeaway when looking into automation through Artificial Intelligence systems is that the possibilities are truly endless, there’s always different manners to introduce new workflows and increase productivity inside an organization and automation is the right tool that (contrary to popular belief) will not take jobs away but will produce new opportunities for professional growth.

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