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10.1145/3543873.3584660acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
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Locating Faulty Applications via Semantic and Topology Estimation

Published: 30 April 2023 Publication History

Abstract

With the explosion of Internet product users, how to locate the faulty ones from numerous back-end applications after a customer complaint has become an essential issue in improving user experience. However, existing solutions mostly rely on manual testing to infer the fault, severely limiting their efficiency. In this paper, we transform the problem of locating faulty applications into two subproblems and propose a fully automated framework. We design a scorecard model in one stage to evaluate the semantic relevance between applications and customer complaints. Then in the other stage, topology graphs that reflect the actual calling relationship and engineering connection relationship between applications are utilized to evaluate the topology relevance between applications. Specifically, we employ a multi-graph co-learning framework constrained by consistency-independence loss and an engineering-theory-driven clustering strategy for the unsupervised learning of graphs. With semantic and topology relevance, we can comprehensively locate relevant faulty applications. Experiments on the Alipay dataset show that our method gains significant improvements in both model performance and efficiency.

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    cover image ACM Conferences
    WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
    April 2023
    1567 pages
    ISBN:9781450394192
    DOI:10.1145/3543873
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 30 April 2023

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    Author Tags

    1. Graph neural network
    2. faulty applications localization
    3. unsupervised graph learning

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    WWW '23
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    WWW '23: The ACM Web Conference 2023
    April 30 - May 4, 2023
    TX, Austin, USA

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