Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters. Which ONE of the following options is LEAST likely to be a…

The correct answer is C. ML model metrics to evaluate the functional performance. While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are…

Testing, Debugging, and Deployment

Question

Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters. Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?

Options

ADifferent Road Types
BDifferent weather conditions
CML model metrics to evaluate the functional performance
DDifferent features like ADAS, Lane Change Assistance etc.

How the community answered

(32 responses)

A
16% (5)
B
9% (3)
C
72% (23)
D
3% (1)

Explanation

While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.

Topics

#pairwise testing#combinatorial testing#self-driving cars#ML metrics

Community Discussion

No community discussion yet for this question.

Full CT-AI Practice