Reimagining QA: How AI-Driven Quality Engineering Is the Future

Timothy Joseph
Timothy Joseph | March 8, 2022

Reimagining QA: How AI-driven Quality Engineering Is the Future

Modern consumers have everything they need right at their fingertips to interact with organizations. They can research, and purchase software products online, and also share their opinions of the product and overall consumer experience in minutes.

For this reason, they prefer brands that can address their needs, from product quality to delivery to customer service. In fact, recent customer service statistics reveal one out of three American consumers will think about switching to a competitor after just one bad experience.

To understand how consumers interact with the product, more and more companies are relying on artificial intelligence, and implementing it in their quality assurance strategy to deliver high quality software solutions.

If you don’t use artificial intelligence (AI) testing in your quality assurance (QA) efforts, you’re setting yourself up for failure.

This article will discuss everything you need to know about AI-based software testing, including its evolution, dynamics, features, and advantages. Read on to discover what it takes to thrive in today’s business setting.


The Evolution of QA Strategies

The testing methods you use today stem back from the manual waterfall methodology of the 1980s and 1990s. The specialists then defined a coding method to automate user actions, and they had to perform maintenance with each new build.

This process worked well for a while because organizations only required a new build a few times annually. However, as consumer requirements evolved, building the maintenance code became more tedious than creating the software itself.

Surprisingly, you will still see many companies using the waterfall method. Quality engineers may spend months or even years writing tests that take up massive resources.

Despite the new technologies, the process flow is still almost the same. One major difference is that firms today require a new build several times per day.

The idea behind improving the testing world was not to change the process but to enhance it. Today, using AI in software testing helps specialists find bugs faster, drastically lessen scripts, and generate tests autonomously.

With AI in testing, you can achieve the last bit by using technology instead of hiring more automation engineers. You don’t need ten more professionals to enhance your capabilities and brainpower by a thousand percent. That’s the beauty of software testing using artificial intelligence.


How AI Is Changing the Dynamics of Software Testing

The COVID-19 pandemic erased all doubts about the necessity of digital transformation in the current business setting. As consumer demands became increasingly complex, specialists turned to AI in QA testing to move toward greater automation.

From the manual waterfall method testing, companies are now becoming increasingly dependent on machines for test code requirements.

However, note that the minimal human intervention found in AI in QA automation requires professionals to teach machines how to learn and enhance their capabilities. Ultimately, the goal is to develop software that can test, diagnose, and heal itself.


Features of QA Test Tools Driven by AI

Discover why industry leaders like Google, Microsoft, and Facebook have invested billions of dollars for their AI testing processes.

  • Simplified Exploratory Testing

    When you use AI in software testing, it’s possible to achieve desired results using fewer resources and codes. AI systems move at the speed of light and can perform simultaneous learning, test design, and execution activities seamlessly. This technology can easily identify testers and detect system flaws through intelligent assistants.

  • Intelligent Gathering of Requirements

    Requirements gathering is a critical component of any software development project, and it can be a daunting strategy without AI-based software testing. Performing it requires completing tedious tasks like overseeing deliverables, tracking approvals, and creating reports. With technology on your side, you can gain valuable insight on gauging complex systems through predetermined key performance indicators (KPIs).

  • Reuse of Test Cases

    Using existing software development assets and reusing them as well-written test cases is a complex process. However, software testing using artificial intelligence can simplify this activity. Through AI, you can inspect applications, gather vital data, measure load time, and analyze user interface (UI) testing much faster than you ever thought possible.

  • Visual Validation

    When you use AI in testing, you can enjoy many high-tech advantages, including advanced pattern and image recognition capabilities. This feature lets you detect bugs by running visual tests on your UI. Regardless of size and shape, AI can recognize UI controls and understand them at a pixel level.

  • Improved Accuracy

    It’s easy for quality engineers to make errors when using manual testing methods, especially for repetitive tasks. For instance, your team might misunderstand a design notation or type in the wrong variable name. However, with AI in QA testing, you can manage tedious tasks more effectively and boost your project’s accuracy.

  • AI-Powered Error Identification

    An error in the code can stop your software from performing its essential function. Usually, the more defects it has, the harder it is to detect the mistakes in the code. However, by using AI in QA testing, you can easily unearth defects, optimize testing, and forecast failure points. This capability can boost your customer satisfaction while reducing your overall costs.

  • AI-Enabled Visual Testing and Monitoring

    AI enhances test platform capabilities through visual testing and support proficiencies. Through AI in QA automation, you can help your development team release visually perfect, fast, and high-quality products. Also, with technology on your side, you can effectively analyze the results of your regression testing efforts.

  • Maximize Code Coverage With AI

    Your code coverage measurement identified which statements of your code you’ve checked with a test run — and those you haven’t yet. When you plan accordingly, you can achieve maximum test coverage through AI testing. In fact, with the right tools, it’s reasonable to aim for 100% code coverage.

  • Make Quicker Decisions With AI

    One of the best things about using AI in software testing is that you can delve deeper into the root causes of errors in your business performance. You can use tools to view data on your efficiency, capabilities, products and services, and other information right at your fingertips. This feature lets you make well-informed decisions quickly.


Advantages of Using AI and QA

Now that you understand what to expect from AI, it’s time to discuss the advantages of using this technology.

  • Effortless Test Planning and Expedited Timelines

    QA experts spend most of their time planning test case scenarios. Whenever a new version is ready for release, they go through the same tedious process. Automation can help them crawl every screen, saving them a lot of time. As AI testing speeds up this task, it enables your specialists to work on other high-value activities.

  • Effective Test Planning

    What should you look for in a good test plan? For starters, it should provide you with all the information you need to cover and report each phase of the development process. However, it’s prone to human error. Using AI in software testing ensures an error-free operation.

  • Predictive Analytics

    AI has the power to gather, sort, and analyze big data. By using AI in testing, you can study the information you have on existing customers and use it to forecast how their needs and browsing practices will change over time. If you play your cards right, you can use these capabilities to gain a competitive advantage in virtually any industry.

  • Improved Regression Tests

    As an organization, you should be agile. You should be ready for whatever consumers throw your way. Whenever you need fast deployment, regression testing should immediately follow. Manual testing makes this process almost impossible, but AI in QA testing can handle such time-consuming tasks.

  • Improved Test Writing

    Software testing using artificial intelligence drastically elevates the quality of your test cases. With the right tools, you can view real test cases which you can operate and regulate. Dated methods don’t allow developers to analyze additional possibilities, but AI can do this task within seconds.



There is no place for mediocrity in the modern business world. COVID-19 sped up the digital transformation process throughout the globe, and there’s no turning back now. The advancements the world has witnessed over the past years are here to stay — and improve over time.

Outsourcing your QA services is a practical way to address the ever-changing needs of the market without hurting your cash flow. Whether you need automation testing, mobile QA, or security testing services, it’s best to work with experienced professionals who know the industry ins and outs by heart.

Choose a company that’s been around for over two decades, like QASOURCE. We provide organizations of all sizes with a dedicated offshore quality engineering team to deliver thoroughly tested code for various applications. Request a free quote now to future-proof your products and services.


This publication is for informational purposes only, and nothing contained in it should be considered legal advice. We expressly disclaim any warranty or responsibility for damages arising out of this information and encourage you to consult with legal counsel regarding your specific needs. We do not undertake any duty to update previously posted materials.