Multi-Objective White-Box Test Input Selection for Deep Neural Network Model Enhancement

To reveal incorrect behaviors and improve the quality of DNN models through testing, a commonly used approach is to collect massive test inputs and manually label them for model optimization. However, it is labor-intensive and time-consuming to manually label the whole collected test inputs. To reduce the labeling cost, recent studies propose test input selection approaches to select a subset of test inputs for model retraining, thereby improving the performance of DNN models. Existing approaches primarily rely on output probabilities to select error-revealing test inputs. Nevertheless, such approaches select test inputs without the knowledge of underlying mechanisms that lead to incorrect decisions. Furthermore, they fail to consider the diversity of test inputs, limiting the capability of DNN models to learn more diverse features. To address these limitations, this paper proposes MOON, a white-box test input selection approach based on multi-objective optimization. The neuron spectrum is proposed to localize suspicious neurons that contribute to erroneous decisions made by DNN models. MOON formulates the test input selection method into a search-based testing problem. By tailoring a multi-objective optimization algorithm, it guides the search process towards maximizing the outputs of suspicious neurons while promoting diversity in neuron behaviors. An empirical evaluation on three datasets and six DNN models was conducted. The experimental results demonstrate the effectiveness of MOON in localizing suspicious neurons. In addition, MOON significantly outperforms state-of-the-art test selection approaches, with an accuracy improvement rate of up to 236.1%.