Cross-Project Defect Prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous CPDP and heterogeneous CPDP (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this article proposes a Heterogeneous Defect Prediction method based on Feature Disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this article, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.

Large language models have shown promise in biomedical natural language processing, yet their use in extracting structured knowledge from medical scales remains limited. This study introduces MedScaleRE-PF, a novel prompting framework designed for relation extraction in Chinese medical scale texts. The framework combines few-shot in-context learning with retrieval-augmented generation, chain-of-thought prompting, and self-verification strategies to improve contextual understanding and factual consistency. We constructed the CMedS-RE dataset, consisting of 606 full-text articles with 19,051 sentences, 29,359 annotated entities, and 7217 relation instances. Experiments were conducted on two tasks: relational triple extraction (RTE) and relation classification (RC). We evaluated both single-step and multi-step prompting, along with four self-verification strategies: direct (D-SV), stepwise (S-CoT-SV), relation-specific (R-CoT-SV), and stepwise relation-specific (SR-CoT-SV). The best results were achieved with single-step prompting and the R-CoT-SV strategy, yielding F1 scores of 42.58 % for RTE under the 32-shot setting and 65.42 % for RC under the 8-shot setting. Compared to a RAG-only baseline, this configuration improved F1 by 7.59 % on RTE and 1.07 % on RC. Additional experiments demonstrated strong performance under annotation-scarce conditions, achieving 46.99 % F1 on RTE with 20 training articles and 59.87 % on RC with 50 articles. Ablation and error analyses further confirmed that task-specific prompt structure and verification design significantly impact performance under few-shot conditions. MedScaleRE-PF also showed consistent results across multiple LLMs, confirming its stability and generalizability. These findings highlight the effectiveness of combining simple prompting and CoT-inspired verification in domain-specific information extraction. MedScaleRE-PF offers a flexible and structured approach for mining medical scale knowledge and supports prompt-based development in biomedical applications.