# Boosting Operational DNN Testing Efficiency through Conditioning

## TL;DR

This paper introduces a novel DNN testing method that uses conditioning on learned representations to reduce variance and sampling costs, significantly improving testing efficiency with limited labeling resources.

## Contribution

It proposes a new variance reduction technique based on conditioning on neural network representations, enhancing operational DNN testing efficiency.

## Key findings

- Requires about half the labeled data compared to random sampling
- Achieves the same precision with fewer labeled inputs
- Effective across various models and datasets

## Abstract

With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions. A challenge is that the testing often needs to produce precise results with a very limited budget for labeling data collected in field. Viewing software testing as a practice of reliability estimation through statistical sampling, we re-interpret the idea behind conventional structural coverages as conditioning for variance reduction. With this insight we propose an efficient DNN testing method based on the conditioning on the representation learned by the DNN model under testing. The representation is defined by the probability distribution of the output of neurons in the last hidden layer of the model. To sample from this high dimensional distribution in which the operational data are sparsely distributed, we design an algorithm leveraging cross entropy minimization. Experiments with various DNN models and datasets were conducted to evaluate the general efficiency of the approach. The results show that, compared with simple random sampling, this approach requires only about a half of labeled inputs to achieve the same level of precision.