Key points of ai tester

The key points of ai tester mainly include functional testing, performance testing, and security testing.
functional testing
Functional testing aims to verify whether the model works as expected, including the following aspects:
Input output validation: Check whether the input and output of the model meet expectations, such as whether the text generation in NLP tasks is correct.
Task support status: Test the model's support for different tasks, such as Q&A, translation, etc.
Contextual understanding ability: whether the model can handle long texts and maintain dialogue coherence.
Special scenario handling: The model's response when dealing with sensitive topics or incorrect inputs.
performance testing
Performance testing focuses on the running speed and resource utilization of the model:
Response time: The response time in high concurrency situations.
Resource consumption: GPU/CPU usage and memory usage.
Scalability: The performance of a model in a distributed environment, such as the efficiency of multiple GPUs.
Stress testing: stability under long-term high load and ability to handle peak flow.
Safety testing
Security testing focuses on combating attacks and data privacy:
Adversarial attack: Test the robustness of the model to adversarial samples, preventing interference in the text from causing erroneous outputs.
Data privacy: Ensure that training data does not disclose personal information and complies with regulations such as GDPR.
Content security: Check if the model generates harmful or biased content and if there are filtering mechanisms in place.
Permission control: prevent unauthorized access and unauthorized operations.