Dependency extraction is crucial in web API security testing, as it helps identify the required API sequences to exploit a vulnerability. Traditional methods are generally rule-based and require extensive manual analysis of API specification documents by ...

Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporating alternating layers ...

• Provide guidelines of choosing the suitable graph pooling operator for training graph neural networks (GNNs).
• Demonstrate hybrid pooling is more effective for Mixup-based graph learning than standard pooling.
• Demonstrate a smaller ...

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 analyzed 90 papers, including 43 from the software engineering (SE) ...

Code search is a common yet important activity of software developers. An efficient code search model can largely facilitate the development process and improve the programming quality. Given the superb performance of learning the contextual ...

Applying deep learning (DL) to science is a new trend in recent years, which leads DL engineering to become an important problem. Although training data preparation, model architecture design, and model training are the normal processes to build DL models,...

Similar to traditional software that is constantly under evolution, deep neural networks need to evolve upon the rapid growth of test data for continuous enhancement (e.g., adapting to distribution shift in a new environment for deployment). However, it ...