Robotic test and inspection practices are being used in a wide range of applications across a diversity of industries. This reflects the many operational and economic benefits arising from their use which include the ability to automate certain laborious manual methods; operation in hazardous locations; uses in inaccessible environments such as within pipelines; the ability to deploy several different techniques simultaneously and thus detect multiple potential defects; reduced workforce costs; and, very often, more rapid testing and greater data acquisition rates than are possible with human operators.
Data Validation: The effectiveness of AI systems is largely dependent on the quality of training data, including aspects such as bias and variety.
Core Algorithm: The heart of AI systems is built on algorithms, which process data and generate insights. Model validation, learnability, algorithm efficiency and empathy are among the key features of this approach.
Non-functional: Performance and Security Testing: Performance and security testing is integral to AI systems. This also includes aspects such as regulatory compliance.
- Systems Integration Testing: AI systems are designed to operate in the larger context of other systems and to solve specific problems. This requires a holistic assessment of AI systems. Thus, integration testing is of primary importance when multiple AI systems with conflicting goals are deployed together.