Leveraging AI to Bring Down Quality Assurance Costs

Have you wondered how AI can improve quality assurance? Software solutions have come a long way from basic applications to sophisticated systems powered by cutting-edge technologies. And, today the pace of new software and product launches is nothing short of break-neck. This is all taking its toll on software testing and quality assurance.

To keep up with the speed, the industry needs assistance with accelerated delivery, and AI can help QA engineers reach there. An AI-driven approach to quality assurance builds on machine learning capabilities to automate complex tasks and orchestrate quality throughout the entire software development lifecycle.

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Here’s how AI is helping to drive quality assurance.

AI-Powered Code Analysis for Quality Assurance Costs

The cost of fixing a bug depends on when you find it, and the general rule is the earlier, the better. To nip issues in the bud, developers leverage static analysis tools that scan the code to pinpoint errors and vulnerabilities. These tools rely on rule sets manually hard-coded into them.

With the ability to continuously learn from data, AI can significantly improve code analysis by elaborating much more sophisticated rule sets. DeepCode, an AI-powered tool for code review, uses about 250,000 rules compared to 4,000 rules for traditional code analyzers. The underpinning ML algorithms learn from millions of open-source commits not only to reveal potential problems but also to show how others have fixed them.

Automated Test Creation

The number of tests can be overwhelming even for a simple application, least to say for a more sophisticated modern-age software solution. And although AI cannot cover the most complex and creative test scenarios, the technology still can help QA engineers automatically generate a range of tests, and this is arguably the most popular area of application of AI in software testing.

Spidering AI refers to the advanced ML-powered tools that crawl through your application, collecting valuable data, taking snapshots, downloading HTML of every page, measuring load time, and more.

This dataset is then leveraged to train ML algorithms what the expected patterns of your applications are. With every new run, the tool compares the current state to the known parameters and raises a flag if an issue occurs, i.e. the load time is longer than expected.

Companies that leverage AI for intelligent test creation are able to reliably generate up to 80% of code, substantially improving their testing efficiency and cutting test creation time by hours. Along with that, AI can also help with test maintenance activities which take up even more time than writing them.

Effective Regression Test Case Prioritization

When introducing new features or implementing changes, however big or small, you need to make sure that the application continues to function as intended. Regression testing means verifying that these changes have not broken the existing functionality.

Tight deadlines and the race to the market put pressure on efficient regression testing, making QA engineers prioritize the most important test cases. And many companies use AI to do just that. By analyzing the wealth of data from testing reports and debug logs, ML algorithms can quite accurately answer burning questions like:

• Which tests to run to get the greatest coverage?
• What is the minimum number of tests to check changes to the code?
• What areas of application are more subject to risk?
• What failures are new and what are duplicates?

 

Armed with this information, QA engineers can focus their efforts on the most critical issues, reducing the cost of regression testing.

CV-driven visual validation

A subset of artificial intelligence, computer vision enables machines to see and understand the visual world. CV-powered image recognition can significantly augment the testing toolkit for more effective and convenient GUI testing. These capabilities turn out of particular importance for testing applications under active development and with frequent changes to the user interface.

By adding computer vision into the mix, QA engineers can write visual test scripts to validate hundreds of UI elements across platforms and configurations much faster and with less effort. Intelligent CV-powered testing tools enable testers to validate not only static layout elements but also dynamic visual content and quickly spot visual regressions that can ruin the overall user experience.

The bottom line

Artificial intelligence has long proved to be not just a passing fad in software testing. QA engineers leverage the technology to accelerate testing processes, gain new efficiencies, and keep QA costs at bay. From AI-driven code analysis to automated test creation to CV-powered visual validation — QA testers are only scratching the surface of AI’s true potential to revolutionize quality assurance.