QA and AI

Artificial intelligence (AI) has touched an astonishing range of industries.

18:42 12 November 2019

. It’s used in medicine, finance, travel, and education. It’s become integral in training and app and product development. We even use it in our personal lives, from asking Alexa to play our favorite songs to steering clear of pedestrians and other vehicles on the road in driverless cars.

The impact of AI is enormous and will only grow in the years to come. And for quality assurance (QA), it could be a game changer. QA services can benefit from this technology — although some testers fear that it may make their profession obsolete. If you’re considering using software QA outsourcing companies for your next product or you’re a professional in the industry yourself, find out how AI is impacting the testing process and what we’ll see in the future.

AI applications in QA & testing services

Automation is nothing new to the work of software QA services. Many testers automate test cases after initially creating and setting up the processes. This helps save time and, assuming the tests have been set up accurately, enables the testers to catch more bugs because machines are often able to spot errors humans can’t.

AI can use machine learning in these instances, meaning it will learn with every test case and adapt accordingly. That is to say, once it has spotted patterns, it will apply the principles to future scenarios, making the process more accurate and able to spot bugs.

For example, in regression testing, a type of testing that ensures that changes to a program have not negatively impacted or altered other aspects of the product, AI will be able to use its prior knowledge to assess whether the product is still working or if new bugs have been introduced.

Another way AI is impacting QA services is through Natural Language Processing (NLP). NLP is a process by which technology is able to understand natural language, facilitating easier interactions between humans and machines. It’s a subset of AI and has strong implications for testing automation.

QA testers can write simpler test cases without coding that can then, in turn, be understood by automation technology. This will both save time and make the entire testing process less complex for QA professionals.

A changing landscape

The introduction of AI into the testing process means QA companies will need to adapt accordingly. QA testers must learn new skills and or apply their existing skills in different ways, as well as find ways for AI to benefit their organizations. For example:

• The tester should learn how to interact with AI, such as developing an understanding of NLP and how to apply it.
  
  
• Testers must understand how to create AI algorithms and analyze them because scripting and automating tests will depend on them.
  
  
• Software QA services will need to consider different applications of AI in the entire testing process, noting where human error is most likely to occur and how technology could address this or make the process more efficient.
  
  

Will AI replace humans?

While AI will play a fundamental role in the changing nature of testing and software QA outsourcing companies, it cannot replace human testers — at least in the current landscape. Humans are still needed to create, plan, and monitor tests. Moreover, automation is only part of the testing process — humans still need to access aspects of the product such as the user experience, something no machine can gauge as of now.

However, QA services will need to leverage the best of human capabilities, such as innovation and creativity, to explore new ways for AI to improve and refine testing, which will become more involved as machine learning gets more sophisticated.

It’s also useful to consider how AI could aid human testers. One application of AI is to replace the tedious aspects of jobs by performing repetitive tasks, and this can help humans in their roles, allowing them to focus on the more complex aspects of the process that machines can’t address — yet. Furthermore, AI is not perfect, and testers need to be cognizant of its shortcomings. That means continuing to analyze products and the work performed by technology for errors and inaccuracies.

Essentially, in order for AI to benefit QA services, humans need to do their part by learning skills to interact with machines and applying their intelligence to the gaps machine learning can’t address. In the future, we will likely see vast improvements to the world of quality assurance, in a large part due to AI — and people who learn how to leverage it.