research-article

Inferring dependency constraints on parameters for web services

Authors:

Guangtai Liang,

Qianxiang Wang,

Hong MeiAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 1421 - 1432

https://doi.org/10.1145/2488388.2488512

Published: 13 May 2013 Publication History

Abstract

Recently many popular websites such as Twitter and Flickr expose their data through web service APIs, enabling third-party organizations to develop client applications that provide function-alities beyond what the original websites offer. These client appli-cations should follow certain constraints in order to correctly in-teract with the web services. One common type of such constraints is Dependency Constraints on Parameters. Given a web service operation O and its parameters Pi, Pj, these constraints describe the requirement on one parameter Pi that is dependent on the conditions of some other parameter(s) Pj. For example, when requesting the Twitter operation "GET statuses/user_timeline", a user_id parameter must be provided if a screen_name parameter is not provided. Violations of such constraints can cause fatal errors or incorrect results in the client applications. However, these con-straints are often not formally specified and thus not available for automatic verification of client applications. To address this issue, we propose a novel approach, called INDICATOR, to automatically infer dependency constraints on parameters for web services, via a hybrid analysis of heterogeneous web service artifacts, including the service documentation, the service SDKs, and the web services themselves. To evaluate our approach, we applied INDICATOR to infer dependency constraints for four popular web services. The results showed that INDICATOR effectively infers constraints with an average precision of 94.4% and recall of 95.5%.

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Hu TYe GTang ZTan SWang HLi MWang ZChristakis MPradel M(2024)UPBEAT: Test Input Checks of Q# Quantum LibrariesProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652120(186-198)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3652120
Xie DLi YKim MPham HTan LZhang XGodfrey MRyu SSmaragdakis Y(2022)DocTer: documentation-guided fuzzing for testing deep learning API functionsProceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3533767.3534220(176-188)Online publication date: 18-Jul-2022
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Lei ZChen YYang YXia MQi Z(2022)Bootstrapping Automated Testing for RESTful Web ServicesIEEE Transactions on Software Engineering10.1109/TSE.2022.318266349:4(1561-1579)Online publication date: 14-Jun-2022
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Index Terms

Inferring dependency constraints on parameters for web services
1. General and reference
  1. Cross-computing tools and techniques
    1. Reliability
    2. Validation
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation

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Published In

cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Hu TYe GTang ZTan SWang HLi MWang ZChristakis MPradel M(2024)UPBEAT: Test Input Checks of Q# Quantum LibrariesProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652120(186-198)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3652120
Xie DLi YKim MPham HTan LZhang XGodfrey MRyu SSmaragdakis Y(2022)DocTer: documentation-guided fuzzing for testing deep learning API functionsProceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3533767.3534220(176-188)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1145/3533767.3534220
Lei ZChen YYang YXia MQi Z(2022)Bootstrapping Automated Testing for RESTful Web ServicesIEEE Transactions on Software Engineering10.1109/TSE.2022.318266349:4(1561-1579)Online publication date: 14-Jun-2022
https://dl.acm.org/doi/10.1109/TSE.2022.3182663
Martin-Lopez ASegura SMuller CRuiz-Cortes A(2022)Specification and Automated Analysis of Inter-Parameter Dependencies in Web APIsIEEE Transactions on Services Computing10.1109/TSC.2021.305061015:4(2342-2355)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSC.2021.3050610
Grent HAkimov AAniche M(2021)Automatically Identifying Parameter Constraints in Complex Web APIs: A Case Study at Adyen2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)10.1109/ICSE-SEIP52600.2021.00016(71-80)Online publication date: May-2021
https://doi.org/10.1109/ICSE-SEIP52600.2021.00016
Mirabella AMartin-Lopez ASegura SValencia-Cabrera LRuiz-Cortes A(2021)Deep Learning-Based Prediction of Test Input Validity for RESTful APIs2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest)10.1109/DeepTest52559.2021.00008(9-16)Online publication date: Jun-2021
https://doi.org/10.1109/DeepTest52559.2021.00008
Martin-Lopez ARothermel GBae D(2020)Automated analysis of inter-parameter dependencies in web APIsProceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings10.1145/3377812.3382173(140-142)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377812.3382173
Santiago DPhillips JAlt PMuras BKing TClarke P(2019)Machine Learning and Constraint Solving for Automated Form Testing2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE.2019.00030(217-227)Online publication date: Oct-2019
https://doi.org/10.1109/ISSRE.2019.00030
Martin-Lopez ASegura SRuiz-Cortés A(2019)A Catalogue of Inter-parameter Dependencies in RESTful Web APIsService-Oriented Computing10.1007/978-3-030-33702-5_31(399-414)Online publication date: 22-Oct-2019
https://doi.org/10.1007/978-3-030-33702-5_31
Hu XFeng ZHuang KChen S(2017)Supporting Interoperability among Web Services Through Efficient Matching2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2017.223(557-566)Online publication date: Jul-2017
https://doi.org/10.1109/COMPSAC.2017.223
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