{"title":"R2API:一种基于hgnn和多任务学习的web API推荐新方法","authors":"Yihui Wang;Xinrou Kang;Xun Li;Shanquan Gao","doi":"10.1109/TSE.2025.3592214","DOIUrl":null,"url":null,"abstract":"Mashup is an application that implements specific functions by integrating one or more web APIs, which are capable of providing services or data on the Internet, thus avoiding the behavior of repeatedly building wheels. With the number of web APIs on various platforms being vast, identifying the suitable web APIs for mashups has become a challenging problem for developers. In this case, researchers propose many methods to recommend available web APIs to mashup developers according to their requirements. Given that the high-order interactions between data are crucial for the recommendation tasks, this work proposes a novel web API recommendation method called R2API. R2API constructs a series of homogeneous hypergraphs from historical data and then utilizes multiple HGNNs (Hypergraph Neural Networks) to learn the vectors for nodes in the hypergraphs. HGNN excels in capturing the high-order interactions between data while effectively mitigating the over-smoothing problem. To reduce the impact of noise and atypical features in historical data and enhance the quality of node vectors, R2API adopts a multi-task joint training strategy to train all HGNNs simultaneously. Meanwhile, R2API assigns semantic vectors to nodes in the hypergraphs during HGNN training to further improve the quality of node vectors. When faced with a specific requirement, R2API identifies its related mashup nodes in the hypergraphs and learns the requirement vector based on the vectors of these nodes, so as to complete the work of web API recommendation. Experiments conducted on the ProgrammableWeb and GitHub datasets show that R2API achieves superior performance compared to baseline methods.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"51 9","pages":"2549-2565"},"PeriodicalIF":5.6000,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"R2API: A Novel Method for Web API Recommendation by Using HGNNs With Multi-Task Learning\",\"authors\":\"Yihui Wang;Xinrou Kang;Xun Li;Shanquan Gao\",\"doi\":\"10.1109/TSE.2025.3592214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mashup is an application that implements specific functions by integrating one or more web APIs, which are capable of providing services or data on the Internet, thus avoiding the behavior of repeatedly building wheels. With the number of web APIs on various platforms being vast, identifying the suitable web APIs for mashups has become a challenging problem for developers. In this case, researchers propose many methods to recommend available web APIs to mashup developers according to their requirements. Given that the high-order interactions between data are crucial for the recommendation tasks, this work proposes a novel web API recommendation method called R2API. R2API constructs a series of homogeneous hypergraphs from historical data and then utilizes multiple HGNNs (Hypergraph Neural Networks) to learn the vectors for nodes in the hypergraphs. HGNN excels in capturing the high-order interactions between data while effectively mitigating the over-smoothing problem. To reduce the impact of noise and atypical features in historical data and enhance the quality of node vectors, R2API adopts a multi-task joint training strategy to train all HGNNs simultaneously. Meanwhile, R2API assigns semantic vectors to nodes in the hypergraphs during HGNN training to further improve the quality of node vectors. When faced with a specific requirement, R2API identifies its related mashup nodes in the hypergraphs and learns the requirement vector based on the vectors of these nodes, so as to complete the work of web API recommendation. Experiments conducted on the ProgrammableWeb and GitHub datasets show that R2API achieves superior performance compared to baseline methods.\",\"PeriodicalId\":13324,\"journal\":{\"name\":\"IEEE Transactions on Software Engineering\",\"volume\":\"51 9\",\"pages\":\"2549-2565\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Software Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11095768/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Software Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11095768/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
R2API: A Novel Method for Web API Recommendation by Using HGNNs With Multi-Task Learning
Mashup is an application that implements specific functions by integrating one or more web APIs, which are capable of providing services or data on the Internet, thus avoiding the behavior of repeatedly building wheels. With the number of web APIs on various platforms being vast, identifying the suitable web APIs for mashups has become a challenging problem for developers. In this case, researchers propose many methods to recommend available web APIs to mashup developers according to their requirements. Given that the high-order interactions between data are crucial for the recommendation tasks, this work proposes a novel web API recommendation method called R2API. R2API constructs a series of homogeneous hypergraphs from historical data and then utilizes multiple HGNNs (Hypergraph Neural Networks) to learn the vectors for nodes in the hypergraphs. HGNN excels in capturing the high-order interactions between data while effectively mitigating the over-smoothing problem. To reduce the impact of noise and atypical features in historical data and enhance the quality of node vectors, R2API adopts a multi-task joint training strategy to train all HGNNs simultaneously. Meanwhile, R2API assigns semantic vectors to nodes in the hypergraphs during HGNN training to further improve the quality of node vectors. When faced with a specific requirement, R2API identifies its related mashup nodes in the hypergraphs and learns the requirement vector based on the vectors of these nodes, so as to complete the work of web API recommendation. Experiments conducted on the ProgrammableWeb and GitHub datasets show that R2API achieves superior performance compared to baseline methods.
期刊介绍:
IEEE Transactions on Software Engineering seeks contributions comprising well-defined theoretical results and empirical studies with potential impacts on software construction, analysis, or management. The scope of this Transactions extends from fundamental mechanisms to the development of principles and their application in specific environments. Specific topic areas include:
a) Development and maintenance methods and models: Techniques and principles for specifying, designing, and implementing software systems, encompassing notations and process models.
b) Assessment methods: Software tests, validation, reliability models, test and diagnosis procedures, software redundancy, design for error control, and measurements and evaluation of process and product aspects.
c) Software project management: Productivity factors, cost models, schedule and organizational issues, and standards.
d) Tools and environments: Specific tools, integrated tool environments, associated architectures, databases, and parallel and distributed processing issues.
e) System issues: Hardware-software trade-offs.
f) State-of-the-art surveys: Syntheses and comprehensive reviews of the historical development within specific areas of interest.