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research-article

Diversity-driven automated web API recommendation based on implicit requirements

Authors:

Lianyong QiAuthors Info & Claims

Volume 136, Issue C

https://doi.org/10.1016/j.asoc.2023.110137

Published: 01 March 2023 Publication History

Abstract

With the rapid growth of the web API sharing community, Mashup development has become a popular way for software developers to quickly create Mashup applications. Mashup development allows developers to quickly implement the functionality they want by combining a chosen set of web APIs, which greatly improves development efficiency. However, the existence of numerous web API candidates make it difficult to select the appropriate API quickly. Most existing automated web API recommendation approaches focus on the developer’s description of requirements but typically overestimate the developer’s ability to fully describe their application’s requirements, thus ignoring their implicit and diversified functional requirements. In this paper, we propose a novel automated approach called DI-RAR to retrieve and recommend web API groups for Mashup creation. Specifically, we formulate an automated web API recommendation task as a nondeterministic polynomial problem. First, a self-attention model assigns weights to query to distinguish the core and non-core requirements. then Dynamic planning retrieval generates steiner trees to retrieve API groups and uncovers strongly related implicit requirements to enrich the mashup’s functions. Finally we apply simhash technique to filter similar results and finally provide diverse API groups. experiments based on a real-world dataset are performed to demonstrate the feasibility and efficiency of DI-RAR.

Highlights

•

We distinguish core requirements from non-core requirements using a self-attention mechanism with query keywords provided by developers to improve recommendation accuracy.

•

A weighted Mashup correlation graph is constructed to model the functional correlation between Mashups, on which Steiner tree evolutionary retrieval is performed to discover the implicit requirements by associating functionally related Mashups.

•

We recommend more diverse API groups to developers by differentiating them using the Simhash technique to satisfy developers’ diversified functional requirements.

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Cited By

Chen ZXie DWang XYou DShen L(2025)High-order complementary cloud application programming interface recommendation with logical reasoning for incremental developmentEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109698140:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.engappai.2024.109698
Liu YZhou XKou HZhao YXu XZhang XQi L(2023)Privacy-preserving Point-of-interest Recommendation based on Simplified Graph Convolutional Network for Geological TravelingACM Transactions on Intelligent Systems and Technology10.1145/362067715:4(1-17)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1145/3620677

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 136, Issue C

Mar 2023

1100 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2023

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Cited By

Chen ZXie DWang XYou DShen L(2025)High-order complementary cloud application programming interface recommendation with logical reasoning for incremental developmentEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109698140:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.engappai.2024.109698
Liu YZhou XKou HZhao YXu XZhang XQi L(2023)Privacy-preserving Point-of-interest Recommendation based on Simplified Graph Convolutional Network for Geological TravelingACM Transactions on Intelligent Systems and Technology10.1145/362067715:4(1-17)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1145/3620677

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