AudioTest: Prioritizing Audio Test Cases

Audio classification systems, powered by deep neural networks (DNNs), are integral to various applications that impact daily lives, like voice-activated assistants. Ensuring the accuracy of these systems is crucial since inaccuracies can lead to significant security issues and user mistrust. However, testing audio classifiers presents a significant challenge: the high manual labeling cost for annotating audio test inputs. Test input prioritization has emerged as a promising approach to mitigate this labeling cost issue. It prioritizes potentially misclassified tests, allowing for the early labeling of such critical inputs and making debugging more efficient. However, when applying existing test prioritization methods to audio-type test inputs, there are some limitations: 1) Coverage-based methods are less effective and efficient than confidence-based methods. 2) Confidence-based methods rely only on prediction probability vectors, ignoring the unique characteristics of audio-type data. 3) Mutation-based methods lack designed mutation operations for audio data, making them unsuitable for audiotype test inputs. To overcome these challenges, we propose AudioTest, a novel test prioritization approach specifically designed for audio-type test inputs. The core premise is that tests closer to misclassified samples are more likely to be misclassified. Based on the special characteristics of audio-type data, AudioTest generates four types of features: time-domain features, frequency-domain features, perceptual features, and output features. For each test, AudioTest concatenates its four types of features into a feature vector and applies a carefully designed feature transformation strategy to bring misclassified tests closer in space. AudioTest leverages a trained model to predict the probability of misclassification of each test based on its transformed vectors and ranks all the tests accordingly. We evaluate the performance of AudioTest utilizing 96 subjects, encompassing natural and noisy datasets. We employed two classical metrics, Percentage of Fault Detection (PFD) and Average Percentage of Fault Detected (APFD), for our evaluation. The results demonstrate that AudioTest outperforms all the compared test prioritization approaches in terms of both PFD and APFD. The average improvement of AudioTest compared to the baseline test prioritization methods ranges from 12.63% to 54.58% on natural datasets and from 12.71% to 40.48% on noisy datasets. CCS Concepts: • Software and its engineering → Software testing and debugging; • Computing methodologies → Neural networks.