Inferring dependency constraints on parameters for web services

  • Authors:

  • Qian Wu;Ling Wu;Guangtai Liang;Qianxiang Wang;Tao Xie;Hong Mei

  • Affiliations:

  • Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China;North Carolina State University, Raleigh, USA;Peking University, Beijing, China

  • Venue:
  • Proceedings of the 22nd international conference on World Wide Web
  • Year:
  • 2013

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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%.