The fast-growing webapi landscape brings clients more options than ever before-in theory. In practice, they cannot easily switch between different providers offering similar functionality. We discuss a vision for dev...
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The fast-growing webapi landscape brings clients more options than ever before-in theory. In practice, they cannot easily switch between different providers offering similar functionality. We discuss a vision for developing webapis based on reuse of interface parts called features. Through the introduction of five design principles, we investigate the impact of feature-based reuse on webapis. Applying these principles enables a granular reuse of client and server code, documentation, and tools. Together, they can foster a measurable ecosystem with cross-api compatibility, opening the door to a more flexible generation of web clients.
Automated service classification is the foundation for service discovery and service composition. Currently, many existing methods extracting features from functional description documents suffer the problem of data s...
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ISBN:
(纸本)9781728187860
Automated service classification is the foundation for service discovery and service composition. Currently, many existing methods extracting features from functional description documents suffer the problem of data sparsity. However, beside functional description documents, the web api ecosystem has accumulated a wealth of information that can be used to improve the accuracy of web service (api) classification. At the moment, there is an absence of a unified way to combine functional description documents with other sources of information (e.g., attributes, interactions and external knowledge) accumulated in the web api ecosystem for api classification. To address this issue, we present a dual-GCN framework that can effectively suppress the noise propagation of textual contents by distinguishing functional description documents and other sources of information (specifically Mashup-api coinvocation patterns by default in this paper) for api classification. This framework is extensible with the ability to include different sources of information accumulated in the web api ecosystem. Comprehensive experiments on a real-world public dataset demonstrate that our proposed method can outperform various representative methods for api classification.
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