In an economy dominated by apps, ensuring software quality in the fastest and most effective way possible is crucial. Skipping quality assurance (QA) can cost you time, money, and even your brand reputation. And today, it’s not enough to do just QA — you have to do QA fast or risk having the competition beat you to your market.

Like in many other aspects of enterprise technology, Artificial Intelligence (AI) and Machine Learning (ML) are set to accelerate QA practices to respond to the business’s demand for speedy and quality QA.

Integrating AI and ML into QA

As companies become more Agile in their way of working, so did modern practices like test automation flourish. Teams launch apps fast and iterate often, and they can do so with quality thanks to test automation. The integration of AI and ML into QA changes the game even more aggressively.

Here are more ways AI and ML can help QA:

• Writing test cases. While automated testing has accelerated the QA process for years, writing test scripts takes precious time. AI and ML can automatically write extensive test cases based on the usage scenarios that they will learn about the application. There’s no need for human intervention — all you need to do is point at the software and let the machine do the heavy lifting. It will also compare the app’s performance with known patterns and flag any deviances in performance, visuals, or loading times.
• Automation without user interface. Generate tests in non-functional aspects of the app, such as unit integration, performance, security, and vulnerability. Apply AI and ML to the source code and monitoring system to access bug prediction, early notification, self-healing, and auto-scaling capabilities.
  
• Self healing automation scripts. Changes like renaming fields or resizing them may seem minor, but they can cause your tests to fail — an experience too familiar to many QA engineers. AI can solve this problem swiftly and effectively by automatically correcting code whenever software developers make these tiny, difficult-to-track changes.

Why AI and ML in Testing?

• Improve accuracy. AI-driven testing enhances accuracy by minimizing human errors and conducting comprehensive tests. With ML, the system continuously learns from the data it collects and refines the testing process for more efficient outcomes.
• Speed up test case creation. AI converts manual test cases into automated ones while significantly reducing time and effort by QA engineers. Accelerate test case creation without sacrificing coverage.
• Go to market faster. Beyond facilitating a faster QA process, AI and ML take learnings from past implementations to streamline workflows, ensuring that every test execution is efficient and effective, leading to a quicker time-to-market.

As a result of all these benefits, your QA team can be more strategic about approaching their goals and initiatives, elevating your organization’s overall productivity and performance, and positively impacting your revenue and growth.

Be one of the early adopters of AI and ML in testing, and reap its benefits early in the game. Explore your options with Stratpoint by filling out the form below.