In Blog, Most Recent, AI and Machine Learning in Test Automation, Manual Testing

Is AI Replacing Manual Testers?

AI and machine learning are the hottest technology buzzwords of today. At the forefront of digital transformation, AI has already begun to revolutionize a wide range of industries. As this technology gets more and more sophisticated, the tasks assigned to machines will only continue to grow. This opens up the door for greater work efficiency, speed, and accuracy.

Machine Learning Meme

Yet for manual testers, many hear the words “machine learning” with a mix of skepticism and apprehension. At best, many software testers don’t think that machine learning technology today isn’t advanced enough to live up to the hype. Similarly to test automation, AI is being propped up as a be-all-end-all solution to the various problems that come up in software testing. At worst, other testers believe that AI is another technological phenomenon that is threatening to take their jobs. Just as people thought (incorrectly) that test automation would render manual testing obsolete, many are concerned that AI is starting a similar trend.

When tackling this broad question of whether or not AI is going to replace testers, it’s important to first look at the test automation tools that incorporate machine learning technology into their platforms. This blog post will take a more in-depth look at AI in test automation, as well as offer tips for how to stay up-to-date in this new age of technology.

Understand what AI is trying to achieve

Contrary to popular belief, machine learning technology is not something that can take over the entire test automation process. Similarly to test automation itself, AI has its time and place in the software development lifecycle. Just like there is no such thing as 100% automation, there is no such thing as test automation that is 100% AI-based.

There are many ways that test automation tools can use AI-based technology, but many focus on achieving one goal: relieving testers of the more routine, repetitive, and error-prone aspects of software testing. One area that test automation tools have determined that they can make a positive impact is by helping reduce test automation maintenance.

Test maintenance and machine learning

Test maintenance is considered one of the largest costs of running automated tests. In a recent survey that we conducted with 200+ testers, 50% of them said that test maintenance is the largest bottleneck that they suffer from. Spending too much time on test maintenance impacts other bottlenecks that these testers mention as well, such as increasing coverage and keeping up in an agile environment.

Testing bottlenecks chart

TestCraft’s survey of over 200 testers. Participants were allowed to give multiple answers.

Today, there are test automation vendors who are using AI to address this issue of test maintenance. At TestCraft specifically, our machine learning technology functions as a self-healing mechanism, overcoming changes in the application automatically without any human involvement. Using machine learning for test maintenance, testers no longer have to go into the code manually, identify the change that happened and update the test flow accordingly. Instead, tests become more resilient, reducing the time and resources needed to do proper test maintenance. This also creates a positive ripple effect, creating more time to focus on other important bottlenecks that these testers face.

Far from taking over testers’ jobs, AI-based test automation tools are looking for ways to make testers’ jobs easier and more enjoyable. By addressing significant bottlenecks that come up in test automation, such as test maintenance, testers can spend less time focusing on old test flows and start testing more widely and in-depth. Yet this raises another question: what can testers practically do to take full advantage of this new technology?

How to take advantage of AI-based test automation tools

After understanding some of the benefits that come with AI test automation tools, there are a few smart ways that testers can stay relevant in this age of new technology. Here are a few options:

Determine your pain point

The first way to take advantage of AI-based test automation is by determining how AI can impact your specific testing environment. As mentioned previously, test automation tools use AI to simplify a variety of functions. While some (like TestCraft) focus on test maintenance, others focus on adding AI to activities that surround testing, such as dashboarding or analytics. Just like it’s important to understand your use case when investing in a test automation tool, it’s also crucial to make the same considerations regarding their machine learning capabilities.

Stay informed about future AI test automation trends

Another great way to stay relevant in the age of AI in test automation is by making it a priority to learn about new trends that come up in this space. AI is still fairly young in terms of its technological maturity, and companies are regularly figuring out how to build upon this technology in all kinds of different ways.

One major advantage that manual testers have over other teams involved in software development is their broad level of business knowledge. When this knowledge is combined staying up-to-date on the latest technology, testers will have an even more unique perspective that can help them stand at the forefront of the company’s future growth.

Some great ways to stay on top of these trends include:

  • Following AI blogs. Popular ones include AI Trends and TopBots, which discuss AI through the lens of many industries, including test automation.
  • Setting up relevant Google Alerts. This is a surefire way to keep up with all the latest news about artificial intelligence.
  • Learning from the vendors. Feel free to ask questions about how vendors’ AI capabilities work. We’re friendly, we promise!

Ask for results

Finally, it’s important that testers hold these AI testing tools accountable. While vendors claim that its machine learning capabilities will make testers’ lives easier, it’s important to ask for the relevant data to back this up. The important metrics to ask for are the tool’s accuracy rates and misclassification rates. Simply put, testers should ask how often it accomplishes its task accurately, and how often it is either unsuccessful or produces false positives. Any test automation vendor should be able to calculate these easily and supply you with the relevant information.

How to Calculate Accuracy & Misclassification Rates for an AI-Based Test Automation Tool

AI in test automation is not an obstacle, but an opportunity

Going back to the original question posed in this article, the notion that AI is replacing manual testers could not be further from the truth. Instead, the machine learning found in test automation tools can actually help testers better achieve their test automation goals. Only time will tell how AI technology will grow and further impact the industry.

 

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