With the vast range of available services, it has become an important challenge to recommend the optimal service for mashup developer. Recent studies are mainly limited by the service similarity, resulting in challenges such as discrepancy in textual semantics, implicity of inter-service relationships, and the sparsity of historical interactions. Service bundles, which offer a set of services, present a novel approach to address the mashup development problem. In this work, we propose an innovative message-passing model, a Heterogeneous Graph AutoEncoderbased service bundle recommendation model (HGAE), to tackle the issues. Specifically, we introduce the Graph Propagation Module to encode potentially implicit semantic relations in the Mashup-Service-Bundle heterogeneous graph. Furthermore, we build a unified representation for the bundle in the Bundle Prediction Module by combining an autoencoder and spatial attention mechanism, enabling the integration of relationships across different node and edge types. Extensive experiments on real-world datasets demonstrate that HGAE notably outperforms state-of-the-art methods on all metrics, with improvements of 8.69% in NDCG and 9.55% in Recall on the ProgrammableWeb dataset.

Software defect prediction (SDP) has been a prominent area of research in software engineering. Previous SDP methods often struggled in industrial applications, primarily due to the need for sufficient historical data. Thus, clustering‐based unsupervised defect prediction (CUDP) and cross‐project defect prediction (CPDP) emerged to address this challenge. However, the former exhibited limitations in capturing semantic and structural features, while the latter encountered constraints due to differences in data distribution across projects. Therefore, we introduce a novel framework called improved clustering with graph‐embedding‐based features (IC‐GraF) for SDP without the reliance on historical data. First, a preprocessing operation is performed to extract program dependence graphs (PDGs) and mark distinct dependency relationships within them. Second, the improved deep graph infomax (IDGI) model, an extension of the DGI model specifically for SDP, is designed to generate graph‐level representations of PDGs. Finally, a heuristic‐based k‐means clustering algorithm is employed to classify the features generated by IDGI. To validate the efficacy of IC‐GraF, we conduct experiments based on 24 releases of the PROMISE dataset, using F‐measure and G‐measure as evaluation criteria. The findings indicate that IC‐GraF achieves 5.0%−42.7% higher F‐measure, 5%−39.4% higher G‐measure, and 2.5%−11.4% higher AUC over existing CUDP methods. Even when compared with eight supervised learning‐based SDP methods, IC‐GraF maintains a superior competitive edge.

With the growth of cloud computing, a large number of innovative mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed for Mashup creation, but most either treat different feature factor interactions equally or solely rely on requirements for API recommendation. These approaches face several challenges such as API compatibility dependencies, ambiguous definition and boundary dilemmas of APIs, and sparse API invocation records. In this work, we propose a Semantic-Enhanced Heterogeneous Graph Network(SEHGN) for Mashup creation. To address the above deficiencies, we design a multi-semantic aggregator to capture semantic associations between features to encode multiple node-edge relationships. Then, we introduce a semantic embedding component to generate text embedding vectors for mashups and APIs to learn global and local semantic information about text documents at different levels of abstraction. Finally, we fuse the output vectors to obtain a list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that SEHGN outperforms state-of-the-art models in terms of overall and long-tail Web API recommendations.

With the increasing development of cloud computing, a large number of innovative Mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed in mashup creation, but most either assign equal weight to model factorization interactions or solely rely on requirements information for API recommendation. Unfortunately, these methods face several challenges, such as explicit and implicit dependencies among APIs, ambiguous API semantics, and the undervaluation of tail APIs. In this work, we propose a Functional and Structural Fusion Model (FSFM) based on Web API recommendation for Mashup creation. To address the above deficiencies, we first design the structural interaction component to encode the latent structural relationships in the heterogeneous network of Mashups and APIs and capture the topological structure signals between different Mashups and APIs. Then, we introduce the functional semantic component to generate text embedding vectors for Mashups and APIs, enhancing their requirement semantics at multiple levels of abstraction. Finally, we fuse the output vectors to obtain the list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that FSFM outperforms other state-of-the-art models in both overall and long-tail Web API recommendations.