This article presents a comprehensive survey on test optimization in deep neural network
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …

The text-to-image models based on diffusion processes, capable of transforming text
descriptions into detailed images, have widespread applications in art, design, and beyond …

In the testing-retraining pipeline for enhancing the robustness property of deep learning (DL)
models, many state-of-the-art robustness-oriented fuzzing techniques are metric-oriented …

To foster the verifiability and testability of deep neural networks (DNN), an increasing
number of methods for test case generation techniques are being developed. When …

Background: Fairness testing for deep learning systems has been becoming increasingly
important. However, much work assumes perfect context and conditions from the other parts …

Neural networks (NNs) are known to have diverse defects such as adversarial examples,
backdoor and discrimination, raising great concerns about their reliability. While NN testing …

Due to the vast testing space, the increasing demand for effective and efficient testing of
deep neural networks (DNNs) has led to the development of various DNN test case …

Even though neural network (NN) programs are often written in Python, using general-
purpose test-generation tools for Python to test them is likely to be ineffective, as these tools …

A Complete Bibliography of ACM Transactions on Software Engineering and Methodology
Page 1 A Complete Bibliography of ACM Transactions on Software Engineering and …