Application Programming Interface (API) migration is a common task for adapting software across different programming languages and platforms, where manually constructing the mapping relations between APIs is indeed time-consuming and error-prone. To facilitate this process, many automated API mapping approaches have been proposed. However, existing approaches were mainly designed and evaluated for mapping APIs of statically-typed languages, while their performance on dynamically-typed languages remains unexplored. In this article, we conduct the first extensive study to explore existing API mapping approaches’ performance for mapping APIs in dynamically-typed languages, for which we have manually constructed a high-quality dataset. According to the empirical results, we have summarized several insights. In particular, the source code implementations of APIs can significantly improve the effectiveness of API mapping. However, due to the confidentiality policy, they may not be available in practice. To overcome this, we propose a novel API mapping approach, namedMatl, which leverages the transfer learning technique to learn the semantic embeddings of source code implementations from large-scale open-source repositories and then transfers the learned model to facilitate the mapping of APIs. In this way,Matlcan produce more accurate API embedding of its functionality for more effective mapping without knowing the source code of the APIs. To evaluate the performance ofMatl, we have conducted an extensive study by comparingMatlwith state-of-the-art approaches. The results demonstrate thatMatlis indeed effective as it improves the state-of-the-art approach by at least 18.36% for mapping APIs of dynamically-typed language and by 30.77% for mapping APIs of the statically-typed language.

DL frameworks are the basis of constructing all DL programs and models, and thus their bugs could lead to the unexpected behaviors of any DL program or model relying on them. Such a wide effect demonstrates the necessity and importance of guaranteeing DL frameworks’ quality. Understanding the characteristics of DL framework bugs is a fundamental step for this quality assurance task, facilitating designing effective bug detection and debugging approaches. Hence, in this work, we conduct the most large-scale study on 1,000 bugs from four popular and diverse DL frameworks (i.e., TensorFlow, PyTorch, MXNet, and DL4J). By analyzing the root causes and symptoms of DL framework bugs associated with five components decomposed from DL frameworks, as well as measuring test coverage achieved by three state-of-the-art testing techniques, we obtain 12 major findings for the comprehensive understanding of DL framework bugs and the current status of existing DL framework testing practice, and then provide a series of actionable guidelines for better DL framework bug detection and debugging. Finally, based on the guidelines, we design and implement a prototype DL-framework testing tool, called TenFuzz , which is evaluated to be effective and finds three unknown bugs on the latest TensorFlow framework in a preliminary study, indicating the significance of our guidelines.

Multimodal person recognition is a common task in video analysis and public surveillance, where information from multiple modalities, such as images and audio extracted from videos, are used to jointly determine the identity of a person. Previous person recognition techniques either use only uni-modal data or only consider shared representations between different input modalities, while leaving the extraction of their relationship with identity information to downstream tasks. Furthermore, real-world data often contain noise, which makes recognition more challenging practical situations. In our work, we propose a novel correlation-based multimodal person recognition framework that is relatively simple but can efficaciously learn supervised information in multimodal data fusion and resist noise. Specifically, our framework learns a discriminative embeddings of persons by joint learning visual features and audio features while maximizing HGR maximal correlation among multimodal input and persons' identities. Experiments are done on a subset of Voxceleb2. Compared with state-of-the-art methods, the proposed method demonstrates an improvement of accuracy and robustness to noise.