Xiaotao Wu
ABSTRACT
The automated run-time detection of feature interactions in Web 2.0 communications applications is an important problem that has not been addressed to date. Such web-enabled communication services are constructed as reusable web widgets that can be composed by users in a web interface. Widgetizing communication features as web services can better serve users with highly customizable features, friendly user interfaces, and easier integration with other web services. However, it also introduces new feature interaction problems. As we show, these composite communication services combine web services and VoIP features in highly dynamic interfaces with application and service state that is typically distributed across multiple domains. In this paper, we present these new feature interactions and propose our solution. We present ten different feature interactions and organize them into a taxonomy. A general method for detecting FIs is given in FOL notation. We also present a coordinator plug-in mechanism for independent widgets to share feature information and state that can be implemented in today's browsers. We finally describe a run-time algorithm for FI detection that is suitable for this architecture and uses the FI notation presented in the paper.
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Index Terms
- Feature interactions between internet services and telecommunication services
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Reviews
Scott Arthur Moody
This paper describes a new approach for automatic detection and conflict analysis of the functions and features of telecommunication services, as they interact with Internet user access. Wu et al. add another dimension to the usual analysis of feature interaction of telecommunication services: constraints introduced by Web services. They introduce a new method for detecting features, described in a feature interaction rule language called FOL. Then, they analyze and collect features in a feature interaction taxonomy, which will benefit future research. These features are used to show the effectiveness of their runtime algorithms in detecting whether the intended results still occur after new features are added. Wu et al. introduce a feature interaction analysis notation and show how the application states and events can be modeled. One way to determine if features interact without conflict is to check the triggers and pre/post conditions, to make sure that they are still consistent when combined. The paper shows how this is accomplished with the authors' analysis algorithms. Example services are then used to show the different constraint conflicts that their analysis methods can detect. For example, if two applications use shared triggers, one feature may block or change the behavior of another feature. In another example, the goals of one feature might negatively impact the goal of another feature. In addition, race conditions, violation of feature assumptions, and other resource contention issues, such as sharing valuable screen space, all complicate unexpected interactions of features. The authors' analysis algorithms are shown, in each case, to automatically help identify these issues, so they can be solved before deployment. Overall, the paper provides some great examples of feature detection, a description of complex feature interaction conflicts, and a set of algorithms that help mitigate these issues. Online Computing Reviews Service
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