- GitHub Repos: Look into OpenAI APIs, TensorFlow or PyTorch libraries for AI integrations.
- Tools: Explore Testim, Applitools, and Mabl, which offer AI-driven testing capabilities with Selenium and Playwright.
The biggest change is when AI can spot and fix test issues on its own. This cuts down on the need to fix things manually and speeds up the process of making new features.
Sectors like finance, healthcare, and online shopping will really feel the squeeze because their systems are complex and they need them to work flawlessly, where AI can make testing easier and more efficient.
AI can now come up with test cases, check if they still work after changes, and predict where problems might pop up. For instance, businesses use AI to do thousands of tests at once, highlighting areas that need a closer look.
AI can look back at test data to figure out why tests keep failing and suggest or even do the fixes. It can find the main reasons for these issues (like timing problems or network issues) and make tests more reliable over time.
AI can automate complex test cases and provide predictive insights into potential failures. The challenge lies in training AI models to handle edge cases and ensuring AI understands the nuances of the business logic.
In addition to traditional QA skills, QAs need to learn AI/ML concepts, automation frameworks, and data analytics. Strong problem-solving and technical acumen will be key.
- Automation expertise: Master automation frameworks like Selenium or Playwright.
- AI understanding: Be comfortable with AI-driven testing tools.
- Collaboration: Work closely with AI specialists, devs, and product teams to ensure AI is properly integrated.