Why use Artificial Intelligence in quality assurance? Why not? AI has been around since the 1950’s, but only recently has it been applied to quality functions. So, what are the benefits of AI-enhanced quality assurance?
- Improved accuracy in testing
- Better defect prediction and prevention
- Improved performance testing
Improved Accuracy in Testing
- Test Prioritization: AI can prioritize test cases based on risk, usage patterns, or changes in the application, ensuring that critical functionalities are tested first.
- Image Recognition: AI-powered tools can verify that the user interface (UI) of an application looks and behaves as expected by comparing screenshots to baseline images.
Enhanced Defect Prediction and Prevention
- Predictive Analytics: AI can analyze historical data to predict areas where defects are likely to occur. This helps in prioritizing testing efforts and focusing on high-risk areas.
- Code Analysis: Machine learning algorithms can review code for potential issues, such as vulnerabilities or code smells, even before testing begins.
- Root Cause Analysis: AI can help in diagnosing the root causes of defects by analyzing logs and patterns, speeding up troubleshooting and resolution
Improved Performance Testing
- Anomaly Detection: AI can monitor performance metrics in real time and detect anomalies or unusual patterns that might indicate performance issues.
- Capacity Planning: Machine learning models can predict future performance needs based on historical data and trends.
Incorporating AI into quality assurance processes can lead to significant improvements in efficiency, coverage, and accuracy, ultimately resulting in higher quality products and faster delivery times.
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Michigan Manufacturing Technology Center
