Why AI Won’t Replace Manual QA Anytime Soon
Now that artificial intelligence (AI) has made its way into nearly every industry, many people are wondering if AI can fully replace manual quality assurance (QA). While AI tools can enhance things like speed and efficiency, it can never replace the most important qualities of QA that rely on a human touch, especially user experiences that are shaped by emotion.
In the world of software development, manual QA remains indispensable, and here’s why.
AI struggles with contextual understanding
While AI excels in areas that feed the system with predefined parameters, it can’t easily interpret subjective elements, like the nuances of user experience or unexpected actions. For example, when clicking a “submit form” button triggers an unrelated action, like opening a link, or when a “save” button refreshes the page without saving the data entered into the forms.
Key examples of unexpected application behaviors AI can’t easily interpret, or that require manual testing, include:
These are some of the most critical application behaviors that can’t be tested with automated, AI solutions. If AI is used to test software, it should be augmented with manual QA.
Manual QA will always reign supreme in these and similar cases. A real human is required to test for these potential conditions because only a human can simulate the real-world conditions that would bring them about. AI systems are likely to miss most, if not all, of these critical issues due to their subtle nature.
AI can’t make human judgment calls
Judgment calls are typically made in the moment based on intuition, emotion, and previous experience. While AI can collect and use historical data, it’s not the same. AI systems function according to their training, and can only identify issues they’ve been specifically trained to recognize. AI is incapable of thinking outside the box, or interpreting situations they haven’t been programmed for.
A great example of this would be an AI system that flags a missing button, but can’t determine if its absence impacts user experience.
This all goes back to AI’s lack of contextual understanding. A large part of QA testing involves interpreting an application’s behavior within a specific context. This requires taking many factors into account, including the application’s purpose, user expectations, and more. AI simply can’t understand the design aesthetics and emotional responses that shape user experience.
Error messages are a perfect example of how this limitation might play out. For instance, a human tester might find an error message to be too technical for users, while an AI system will only check to make sure the error message is displayed. If there is no manual oversight, users could end up frustrated and confused when they receive that error message.
When it comes to AI’s lack of emotion, the stakes are pretty high for skipping manual testing. Many applications are released that have frustrating user interfaces, and it’s pretty clear they haven’t been tested by humans. When users become frustrated with an application, they stop using it and move to a competitor.
Pop-ups are a good example of this potential frustration. Some pop-ups are unobtrusive and can be helpful. Some disrupt the user experience. AI won’t be able to distinguish the difference.
Another example of a situation that requires intuition to discover is when a human discovers an error after testing a rarely used combination of settings based on nothing more than their gut feeling. If AI isn’t programmed to test that exact setting combination because it doesn’t make sense or isn’t likely, it won’t catch the error. The problem is that if something is possible, there will be users who do it, and resulting errors can be a nightmare for the user experience.
Humans are better at identifying infrequent or uncommon issues
Sometimes software applications produce errors even in post-production that only happen once in a while, and humans are better at discerning these situations. Although many infrequent issues tend to be minor, they can be critical for user satisfaction.
One example of this would be a file upload form that only freezes when a user uploads a file of a specific size and format. For instance, a .jpg file over 5 MB. If AI is only programmed to test uploads using files under 5 MB, it will never encounter this issue. However, a human who uploads files of all sizes would naturally encounter this issue.
AI isn’t all that flexible
Some repetitive tasks in the QA process can be automated with AI to improve efficiency, but it’s not easy for an AI system to adapt to a dynamic testing environment with evolving requirements.
Manual QA can adapt to changes in requirements or environments without extensive updates or retraining. An AI model, on the other hand, would need to be retrained extensively for every change.
Since the effectiveness of AI depends on its training data, any slight changes in the application or environment would require collecting and processing plenty of new data to support accuracy. Many of the changes that would necessitate more data collection are unexpected, which would require more time and resources, making automated QA inflexible.
Manual QA is the clear winner for the flexibility needed to handle changing environments.
AI can’t collaborate with teams
Perhaps the biggest and most convincing reason to hold onto manual QA is the importance of collaboration between teams. For example, manual testers frequently need to consult with designers, developers, and stakeholders. AI can’t participate in these discussions, provide feedback, or make suggestions to improve certain functions.
For this reason alone, manual QA will always be an integral part of software testing. Even when a company uses AI automation, there still needs to be a manual element to get the job done.
What can automated QA do well?
Now that we’ve covered the reasons automated QA isn’t an effective standalone solution, you might be wondering how it can be used, and what its strengths are. Here are some of the benefits offered by automated QA.
In a nutshell, AI-powered QA testing can provide value and enhance the overall testing capabilities of a given system. At a basic level, AI can increase speed, efficiency, and accuracy, but it’s best paired with manual QA.
AI-powered QA applications
There are a handful of popular AI testing tools that software developers use every day. Tools like:
Each of these applications have their strengths and weaknesses. For example, Selenium does a good job with identifying dynamic web elements, while Applitools specializes in detecting visual inconsistencies between devices and browsers.
There are also AI-enhanced testing frameworks that are built on machine learning algorithms. These frameworks are designed to create and execute tests, analyze results, and more. Since they provide a highly structured environment, AI algorithms are more effective, which increases the reliability of data. Some of these frameworks include:
These frameworks are considered to be highly effective, and they can play an important role in the QA process. However, due to the inherent limitations of AI to discern certain nuances, human involvement is still required to fully test an application.
Humans will always have a role in QA
No matter how advanced AI becomes, manual QA will remain an essential part of the software testing process. A human touch is required to meet technical needs and user expectations. Real people use skills that AI can’t be programmed to mimic. For example, human testers use intuition, adaptability, emotion, and collaboration to fully test applications.
While AI can complement the QA process by increasing speed and efficiency, it will never fully replicate the human touch that makes QA effective.
Launch your application with peace of mind
Getting ready to launch a new software application? Don’t risk it without fully testing your application first. Even the smallest issue can negatively impact your success.
If you need help with QA testing, we would love to partner with you. Our experienced QA software testing team will thoroughly and vigorously test your application from top to bottom to ensure it’s secure, functional, and meets user expectations.
Get in touch with us today to learn more.
