Issue assignment plays a critical role in open-source software (OSS) maintenance, which involves recommending the most suitable developers to address the reported issues. Given the high volume of issue reports in large-scale projects, manually assigning issues is tedious and costly. Previous studies have proposed automated issue assignment approaches that primarily focus on modeling issue report textual information, developers’ expertise, or interactions between issues and developers based on historical issue-fixing records. However, these approaches often suffer from performance limitations due to the presence of incorrect and missing labels in OSS datasets, as well as the long tail of developer contributions and the changes in developer activity as the project evolves. To address these challenges, we propose IssueCourier, a novel Multi-Relational Heterogeneous Temporal Graph Neural Network approach for issue assignment. Specifically, we formalize five key relationships among issues, developers, and source code files to construct a heterogeneous graph. Then, we further adopt a temporal slicing technique that partitions the graph into a sequence of time-based subgraphs to learn stage-specific patterns. Furthermore, we provide a benchmark dataset with relabeled ground truth to address the problem of incorrect and missing labels in existing OSS datasets. Finally, to evaluate the performance of IssueCourier, we conduct extensive experiments on our benchmark dataset. The results show that IssueCourier can improve over the best baseline up to 45.49% in top-1 and 31.97% in MRR.

For a vulnerability reported as an item of platforms such as CVE or NVD, software maintainers need to submit patches (in the form of code commit) to fix it, which is often performed silently for the sake of keeping products’ reputation or avoiding malicious attacks. But such a silent practice keeps patches hidden from affected downstream software maintainers, thus they have to identify patches in a large corpus of code commits manually, i.e., silent vulnerability patch identification (SVPI). Existing techniques in this field were often developed under the assumption that a vulnerability is matched to one patch, thus output a ranking list that simply reflects the similarity between one individual patch and the vulnerability. However, previous research has demonstrated that many vulnerabilities correspond to more than one patch in practice, this phenomenon largely threatens the effectiveness of existing SVPI techniques because they typically ignore the correlation between patches. In this paper, we propose SHIP, a Silent vulnerability patcH Identification approach suited for multiPle-patch scenarios, to make patches corresponding to a vulnerability no longer isolated islands. For a vulnerability item, we first obtain several highly-relevant code commits by measuring heuristic features, and then employ a large language model (i.e., DeepSeek-V3) to predict both the link between a code commit and the vulnerability as well as the link between a pair of code commits, and thus deliver candidate groups each containing one or more code commits that could be patches of the vulnerability. Finally, we perform the max-pooling strategy on the features of code commit(s) contained in each candidate group to determine the ranking of groups, the Top-1 group will be output. The experimental results demonstrate the promise of SHIP: on the benchmark consisting of 4,631 vulnerability items, it can achieve 84.30%, 59.14%, and 69.51% of Recall, Precision, and F1-Score, respectively, outperforming the state-of-the-art SVPI technique by 37.54%, 28.71%, and 32.35%, respectively.

With the proliferation of data APIs, it is not uncommon that users who have no clear ideas about data APIs will encounter difficulties to build Mashups to satisfy their requirements. In this paper, we present a semantic-based mashup navigation system, sMash that makes mashup building easy by constructing and visualizing a real-life data API network. We build a sample network by gathering more than 300 popular APIs and find that the relationships between them are so complex that our system will play an important role in navigating users and give them inspiration to build interesting mashups easily. The system is accessible at: http://www.dart.zju.edu.cn/mashup.

We present sMash, a system for facilitating users to mashup Web data. The aspects emphasized by the demo are: (1) how to help novice users master data APIs and relationships amongst them easily; (2) how to inspire various users to build more amazing Web data mashups. First, a real-life data API network is constructed and visualized to enable users to surf and mashup. Second, two kinds of recommendations are generated dynamically based on a comprehensive analysis of the network, user's traces and a repository of mashups to provide navigation.

Nowadays, social networks play an important role in our lives, in which information and knowledge are exchanged, shared and transformed. With time, large volumes of real-world data have been accumulated capturing diversified application domains. However, heterogeneity and incompleteness of data make social networks perform as 'data isolated islands' separated to each other. In this paper, we propose a generic approach that consists of an ontology-based social network integration approach and a statistic learning method towards the Semantic Web data. In particular, an extended FOAF (Friend-Of-A-Friend) ontology is used as the mediation schema to integrate social networks and a hybrid entity reconciliation method is used to resolve entities of different data sources. We also present an analyzing approach that learns a probabilistic semantic model (PSM) from social data for relationship identification. Empirical results prove that, compared with single numeric or logical methods respectively, our hybrid reconciliation method has obvious improvements on precision and recall during combining LinkedIn.com and DBLP. In addition, PSM framework can reserve richer semantics of semantic data completely during data analysis.

We conduct the first systematical adoption of the Semantic Web solution in the integration, management, and utilization of TCM information and knowledge resources. As the results, the largest TCM Semantic Web ontology is engineered as the uniform knowledge representation mechanism; the ontology-based query and search engine is deployed, mapping legacy and heterogeneous relational databases to the Semantic Web layer for query and search across database boundaries; the first global herb-drug interaction network is mapped through semantic integration, and the semantic graph mining methodology is implemented for discovering and interpreting interesting patterns from this network. The platform and underlying methodology are proved effective in TCM-related drug usage, discovery, and safety analysis.

BACKGROUND: Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science. RESULTS: We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research. CONCLUSION: Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline.