How Artificial Intelligence Is Impacting Quality Assurance Roles

How Artificial Intelligence Is Impacting Quality Assurance Roles
How Artificial Intelligence Is Impacting Quality Assurance RolesHow Artificial Intelligence Is Impacting Quality Assurance Roles

Recent news headlines have been dominated by the rise of AI, and for good reason. AI has significantly impacted every aspect of civilization more than any other contemporary technological advancement.

As we have seen, technology has acted as a catalyst for the employment of automation to carry out jobs. Additionally, it has made it possible to analyze data at greater depths and to present the results in a way that non-technical consumers can easily understand. Additionally, it enables people to decide quickly and intelligently based on a wealth of data.

While AI has altered the world, the field of quality assurance (QA) has been particularly affected by its consequences. Although there are many advantages to these changes, they also bring new challenges that need to be overcome.

I will examine these advantages and disadvantages in this post to comprehend how AI has affected QA responsibilities generally.

AI’s Advantages for Quality Assurance

Let’s first examine a few ways that AI can assist the QA process:


The majority of the QA process was manual until AI was widely adopted. It took a lot of work for software engineers to make sure an application was of high quality. Even though sophisticated tools were being used, the programmer was still conducting several tests, looking through countless lines of code, and manually implementing fixes.

Despite all of this work, human nature typically allowed certain mistakes to creep into the final output. With AI, regression, functional, and load testing can now be completely automated, freeing engineers from the need to do these tasks manually.

Statistical Analysis

In a conventional setting, mistakes are corrected after they have been made. By examining the code and making modifications before problems arise, AI algorithms may now proactively prevent errors from happening. This has led to a greater change in the QA tester’s responsibility toward data analysis.

User Encounter

Clients won’t use a program again if the user experience is poor. Due to its complexity and subjectivity, testing UX has traditionally been a job left to individuals up until now. However, AI has now developed sufficiently to mimic human behavior. This makes it possible for AI to contribute to UX testing in a practical way, which speeds up the process and may produce better outcomes.

Investigative Testing

This kind of testing allows the tester to freely explore the application rather than adhering to a predetermined path. Now that AI can undertake this job, new flaws and problems with the code that previously would not have been discovered without putting in many hours of manual testing are discovered.

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Using AI for quality assurance presents challenges.

Several of the advantages of employing AI for QA have now been covered. However, AI has not always been a good thing. Now let’s look at some of the challenges that AI has brought about:

Requirements for Data

AI requires a huge amount of properly chosen data to be supplied to it in order for it to understand how to carry out its many tasks. A team of QA experts must curate all of this data. To avoid the AI model being trained erroneously, they must make sure the data being provided to the system is accurate.

Unknown Methods

Generally speaking, a lot of people still do not properly comprehend how AI functions. Even AI experts occasionally struggle to comprehend how the system learns. Additionally, AI-producing companies frequently work to keep their techniques a secret. Uncertainty about how AI operates can cause serious problems.

Continuous Change

Finally, AI is still developing quickly. A QA expert must constantly be on the lookout for the newest developments in order to keep up. This involves becoming familiar with the most recent AI models and how they differ from their forerunners.

This means that although AI can be a tool to help QA professionals do their jobs more effectively, it also requires a lot of effort to master and continue learning about.

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