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  • 2020.2.12 Data-Driven Web APIs Recommendation for Building Web Applications

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Data-Driven Web APIs Recommendation for Building Web Applications

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

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Data-Driven Web APIs Recommendation for Building Web Applications. / Qi, L.; He, Q.; Chen, F. et al.

In: IEEE Transactions on Big Data, Vol. 8, No. 3, 01.06.2022, p. 685-698.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Qi, L, He, Q, Chen, F, Zhang, X, Dou, W & Ni, Q 2022, 'Data-Driven Web APIs Recommendation for Building Web Applications', IEEE Transactions on Big Data, vol. 8, no. 3, pp. 685-698. https://doi.org/10.1109/TBDATA.2020.2975587

APA

Qi, L., He, Q., Chen, F., Zhang, X., Dou, W., & Ni, Q. (2022). Data-Driven Web APIs Recommendation for Building Web Applications. IEEE Transactions on Big Data, 8(3), 685-698. https://doi.org/10.1109/TBDATA.2020.2975587

Vancouver

Qi L, He Q, Chen F, Zhang X, Dou W, Ni Q. Data-Driven Web APIs Recommendation for Building Web Applications. IEEE Transactions on Big Data. 2022 Jun 1;8(3):685-698. Epub 2020 Feb 24. doi: 10.1109/TBDATA.2020.2975587

Author

Qi, L. ; He, Q. ; Chen, F. et al. / Data-Driven Web APIs Recommendation for Building Web Applications. In: IEEE Transactions on Big Data. 2022 ; Vol. 8, No. 3. pp. 685-698.

Bibtex

@article{bf163215c9544c0fa80c7a6e9057b9bf,
title = "Data-Driven Web APIs Recommendation for Building Web Applications",
abstract = "The ever-increasing popularity of web APIs allows app developers to leverage a set of existing APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropriate and compatible web APIs. Currently, app developers usually have to manually discover candidate web APIs, verify their compatibility and select appropriate and compatible ones. This process is cumbersome and requires detailed knowledge of web APIs which is often too demanding. It has become a major obstacle to further and broader applications of web APIs. To address this issue, we first propose a web API correlation graph built on extensive data about the compatibility between web APIs. Then, we propose WAR (Web APIs Recommendation), the first data-driven approach for web APIs recommendation that integrates API discovery, verification and selection operations based on keywords search over the web API correlation graph. WAR assists app developers without detailed knowledge of web APIs in searching for appropriate and compatible APIs by typing a few keywords that represent the tasks required to achieve app developers{\textquoteright} objectives. We conducted large-scale experiments on 18,478 real-world APIs and 6,146 real-world apps to demonstrate the usefulness and efficiency of WAR.",
author = "L. Qi and Q. He and F. Chen and X. Zhang and W. Dou and Q. Ni",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2022",
month = jun,
day = "1",
doi = "10.1109/TBDATA.2020.2975587",
language = "English",
volume = "8",
pages = "685--698",
journal = "IEEE Transactions on Big Data",
issn = "2332-7790",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Data-Driven Web APIs Recommendation for Building Web Applications

AU - Qi, L.

AU - He, Q.

AU - Chen, F.

AU - Zhang, X.

AU - Dou, W.

AU - Ni, Q.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/6/1

Y1 - 2022/6/1

N2 - The ever-increasing popularity of web APIs allows app developers to leverage a set of existing APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropriate and compatible web APIs. Currently, app developers usually have to manually discover candidate web APIs, verify their compatibility and select appropriate and compatible ones. This process is cumbersome and requires detailed knowledge of web APIs which is often too demanding. It has become a major obstacle to further and broader applications of web APIs. To address this issue, we first propose a web API correlation graph built on extensive data about the compatibility between web APIs. Then, we propose WAR (Web APIs Recommendation), the first data-driven approach for web APIs recommendation that integrates API discovery, verification and selection operations based on keywords search over the web API correlation graph. WAR assists app developers without detailed knowledge of web APIs in searching for appropriate and compatible APIs by typing a few keywords that represent the tasks required to achieve app developers’ objectives. We conducted large-scale experiments on 18,478 real-world APIs and 6,146 real-world apps to demonstrate the usefulness and efficiency of WAR.

AB - The ever-increasing popularity of web APIs allows app developers to leverage a set of existing APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropriate and compatible web APIs. Currently, app developers usually have to manually discover candidate web APIs, verify their compatibility and select appropriate and compatible ones. This process is cumbersome and requires detailed knowledge of web APIs which is often too demanding. It has become a major obstacle to further and broader applications of web APIs. To address this issue, we first propose a web API correlation graph built on extensive data about the compatibility between web APIs. Then, we propose WAR (Web APIs Recommendation), the first data-driven approach for web APIs recommendation that integrates API discovery, verification and selection operations based on keywords search over the web API correlation graph. WAR assists app developers without detailed knowledge of web APIs in searching for appropriate and compatible APIs by typing a few keywords that represent the tasks required to achieve app developers’ objectives. We conducted large-scale experiments on 18,478 real-world APIs and 6,146 real-world apps to demonstrate the usefulness and efficiency of WAR.

U2 - 10.1109/TBDATA.2020.2975587

DO - 10.1109/TBDATA.2020.2975587

M3 - Journal article

VL - 8

SP - 685

EP - 698

JO - IEEE Transactions on Big Data

JF - IEEE Transactions on Big Data

SN - 2332-7790

IS - 3

ER -