In an ever shifting landscape of Quality Assurance (QA) tools and processes there is a new contender coming into the forefront: Artificial Intelligence (AI). When I hear people talking about QA and AI the first question that comes to mind is: ‘Will AI replace my job’ and the answer is ‘No, AI will not replace your job’ however AI can be used to enhance your job and can be used as an extra tool in your repertoire. The quote below sums this up perfectly:

“AI won’t replace people, but people using AI will replace those who don’t”

So how can you stay ahead of the curve? By looking into what challenges QA faces now and how we can solve these challenges by using AI.

A couple of QA challenges that any QA Engineer faces in any company are:

Time Constraints

Most QA Engineers, if not all of us, in our careers have experienced managing tasks across multiple projects. The QA Engineer’s traditional approach of writing test cases, creating test suites, testing features, creating bug reports, and submitting weekly reports are all tasks that take time to complete. The more time a QA Engineer spends on these tasks the less time they have for higher-level testing. These time constraints create a bottleneck and AI can be used to reduce these constraints and give more time back to the QA Engineer.

Test Maintenance

As the project takes shape, it is important that the QA Engineer maintains their tests so they do not fall behind. The QA Engineer writes the test cases for new features, modifies existing test cases, and removes test cases that are no longer needed or required which takes time and adds to the Time Constraints mentioned above.

Changing requirements

Have you ever had to go back and update your Test Cases because the requirements changed? Changing requirements also goes hand and hand with Test Maintenance and Time Constraints when the requirements are changed whether after the initial meetings or during mid sprint it is up to the QA Engineer to make sure that the test cases stay relevant and are updated so that any team member can know what is to be expected of them.

Now that I have talked about the QA challenges a little bit, I can finally get to how AI fits into all of this and how these QA challenges can be solved. The list below does not encompass everything but it does give us a little insight into what can be.

Benefits of AI in Quality Assurance:

Efficiency in Test Creation

 Let’s further explore the benefits:

The QA Engineer writes all test cases manually following the requirements of the feature for any sprint. AI can assist in generating test cases by analyzing the requirements of the feature and providing additional test steps, covering potential issues based on previous builds. As the QA Engineer ‘feeds’ the AI tool it is the responsibility of the QA Engineer to make sure that these test steps are related to the test case and also to make sure that it can be tested against. I mentioned time constraints earlier as a challenge, and with assistance from AI this can enable QA Engineers to focus their efforts on other high level testing tasks.

Cost Savings

While AI writes the test cases this gives the QA Engineer more time for thorough testing and less time for repetitive test case writing allowing the QA Engineer to focus on higher level testing tasks. Esther Okafor stated in her presentation titled, ‘The Future of QA: Integrating AI for Intelligent Test Management’ at the OnlineTestConf that ‘Organizations using AI driven testing reported 30% reduction in testing costs and 25% increase in testing efficiency’.

Real Time Monitoring

The QA Engineer tests the application, thoroughly ensuring that there are no bugs, but no matter how extensive our tests are we do not always catch everything. AI can prevent bugs before they are introduced by predicting potential problems based on data from previous builds. This ‘real-time’ monitoring can also assist in identifying patterns from previous builds that on the surface the QA Engineer might miss. For instance, AI can detect degradation in an app’s performance generated by your server logs and user interactions. Automatically catching these issues early can ensure that the user has a positive experience.

To sum this up, AI is not after your job. We will always need human QA Engineers since there are some issues that cannot be done by AI or automation. However, QA Engineers will need to learn how to use AI tools so we do not get left behind in the shuffle. It is better to learn how to use these AI tools to further enhance your QA knowledge and not only for your personal benefit, but to effectively help your company continue to grow in the many years to come.