Reliability
Robustness
Techniques that test or improve whether performance holds when inputs, prompts, data or conditions change.
6 techniques
6 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Safety Envelope Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Safety envelope testing checks how an AI system performs at and beyond the edges of the conditions it was designed for,... | ||
| Prompt Robustness Testing | Testing | Architecture/neural Networks/transformer/llm Paradigm/generative +1 | Text | Prompt robustness testing measures how much a language model's answers, or its performance on a task, change when the... | ||
| Continual Learning Stability Testing | Testing | Architecture/model Agnostic Architecture/neural Networks +1 | Any | Continual learning stability testing checks whether a model that is updated over time with new data or tasks keeps what... | ||
| Multi-Agent System Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Multi-agent system testing examines systems in which several AI agents act on shared tasks or resources, to find... | ||
| Linear Bound Propagation Verification | Algorithmic | Architecture/neural Networks Requirements/white Box | Image Tabular | Linear bound propagation verification proves that a trained neural network meets a stated property for every input in a... | ||
| Metamorphic Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Metamorphic testing checks a model or AI system without needing the correct output for each test input, by checking that... |
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