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Identifying Incompatible Implementations of Industry Standard Service Interfaces for Dependable Service-Based Applications

In this paper we study fault localization techniques for identification of incompatible configurations and implementations in service-based applications (SBAs). We consider SBAs with abstract service interfaces that integrate multiple concrete service implementations from various providers. Practice has shown that standardized interfaces alone do not guarantee compatibility of services originating from different partners. Hence, dynamic runtime instantiations of such SBAs pose a great challenge to reliability and dependability. The aim of this work is to monitor and analyze successful and faulty executions in SBAs, in order to proactively detect incompatible configurations at runtime. Our approach is based on well-established machine learning techniques, and extends state-of-the-art fault localization by explicitly addressing temporary and changing fault conditions. Moreover, the presented fault localization technique works on a per-request basis and is able to take individual service inputs into account. Considering not only the service configuration but also the service input data as a parameter for the fault localization algorithm increases the computational complexity by an order of magnitude. Hence, our extensive performance evaluation is targeted at large-scale SBAs and illustrates the feasibility and decent scalability of the approach.