The Impact of AI on QA Roles: Benefits and Drawbacks Explored
The rise of AI has been dominating news headlines recently — and for good reason. More so than any other recent advances, AI has had tremendous effects on all of society.
We have seen that it has become a catalyst in facilitating the use of automation to complete tasks. It has also allowed for deeper levels of analysis of data and can report its results in a format that can be readily consumed by users without a technical background. Furthermore, it allows for users to make quick and informed decisions based on vast amounts of data.
While AI has changed the world in general, its effects have also made a strong impact on the field of quality assurance (QA). These changes come with immense benefits, but also introduce new obstacles that must be addressed.
In this article, we will take a look at these benefits and drawbacks in order to understand how AI has impacted QA roles overall.
First, let’s look at a few of the ways that AI can support the QA process:
1. Full Automation: Prior to the mass introduction of AI, the QA process was primarily manual. For software developers, ensuring the quality of an application was a painstaking effort. Sure, it involved the use of sophisticated tools, but the programmer was still running numerous tests, searching through lines upon lines of code, and applying fixes one by one. Even with all this effort, some errors often made their way through to the finished product, due to human nature. With AI, developers no longer need to manually run their QA routines or do regression, functional, and load testing — these chores can now be fully automated.
2. Proactive Error Prevention: In a traditional environment, errors are fixed after they have been made. Now, AI algorithms can proactively prevent errors from occurring by analyzing code and making adjustments before errors even happen. Because of this, the role of a QA tester has shifted more into data analysis.
3. Enhanced User Experience Testing: If an application’s UX is sub-par, then customers will not be returning to it. Until now, testing UX has typically been a job relegated to people due to its complexity and subjectivity. Now, though, AI has become advanced enough to copy human interactions. This lets AI become a useful contributor in the testing of UX, which makes the testing faster and can lead to better results.
4. Exploratory Testing: In this type of testing, the tester does not follow a predetermined path but is able to explore the application freely. AI is now able to handle this task and, in doing so, is able to find new bugs and issues with code that previously would not have been found without spending countless hours of manual testing.
We’ve now discussed many of the benefits of using AI for QA. But the presence of AI has not always been positive. Let’s now take a look at some of the difficulties that AI has introduced:
1. Data Curation: AI does not just casually learn how to perform its various functions — it requires a vast amount of carefully selected data to be fed to it. All of this data has to be curated by QA professionals. They must ensure that the data being fed to the system is accurate; otherwise, the AI model will be trained incorrectly.
2. Lack of Understanding: In general, many people still do not fully understand how AI works. Even AI experts don’t always understand how the system is learning. On top of that, the companies that make AI often try to keep their methods secret. Not fully understanding how AI works can become a big issue.
3. Rapid Evolution: Finally, AI is still rapidly evolving. To keep up, a QA professional needs to be constantly on their toes, continually learning about the latest trends. This includes learning about the latest AI models and how they have changed from their predecessors. This means that despite being a tool to help QA professionals more efficiently do their jobs, AI also takes a lot of work to master and keep learning about.
As we can see, AI has been having a big impact on QA roles. It is changing how the QA professional accomplishes their work in positive but also concerning ways. The more learning QA teams do to understand AI, and the more that we learn about it as a society, the greater its rewards will be for QA and for end products.
