Unsupervised domain adaptive hashing has emerged as a promising approach for efficient and memory-friendly cross-domain retrieval. It leverages the model learned on labeled source domains to generate compact binary codes for unlabeled target domain samples, ensuring that semantically similar samples are mapped to nearby points in the Hamming space. Existing methods typically apply domain adaptation techniques to the feature space or the Hamming space, especially pseudo-labeling and feature alignment. However, the inherent noise of pseudo-labels and the insufficient exploration of complementary knowledge across spaces hinder the ability of the adapted model. To address these challenges, we propose a Vision-language model assisted Pseudo-labeling and Dual Space adaptation (VPDS) method. Motivated by the strong zero-shot generalization capabilities of pre-trained vision-language models (VLMs), VPDS leverages VLMs to calibrate pseudo-labels, thereby mitigating pseudo-label bias. Furthermore, to simultaneously utilize the semantic richness of high-dimensional feature space and preserve discriminative efficiency of low-dimensional Hamming space, we introduce a dual space adaptation approach that performs independent alignment within each space. Extensive experiments on three benchmark datasets demonstrate that VPDS consistently outperforms existing methods in both cross-domain and single-domain retrieval tasks, highlighting its effectiveness and superiority.

Summary Metamorphic testing (MT) is proposed to overcome the oracle problem in software testing, and metamorphic relations (MRs) are the core of MT. There is a lack of guidelines for constructing effective MRs, and it is difficult to reuse MRs mainly because most MRs are closely related to the domain knowledge. In this article, we propose a method for constructing MRs from specifications in tabular expression format. Our method constructs MRs according to the characteristics of tabular expressions, especially the relationships between the header grids and the main grid, namely, our method is domain‐independent and the construction process is simplified. In addition, the derived MRs can be applied to specifications with the same tabular expression structure. For specifications with different tabular expression structures, MRs can still be used after slight adjustments. To evaluate the performance of our method in practice, we apply the method to five applications. The experimental results demonstrate that our method is effective for a program with the oracle problem, and that it is applicable to tabular expressions in various formats. Compared with representative testing methods, our method identifies errors that are not detected by the compared methods. Hence, our method and existing methods can complement each other. The MR proposed in this article outperforms MRs constructed based on program properties.