Since their invention as lightweight integration technology about a decade ago, Web Services have matured significantly. Today, major middleware solution vendors as well as industry communities like RosettaNet are propagating Web services even for exchanging business-critical data and implementing inter-organizational business processes. Core enablers for using Web services in this domain are stateful interactions using the Web Services Business Process Execution Language (WS-BPEL) as well as advanced communication features like security and reliability using the WS-Security and WS-ReliableMessaging standard specifications. However, advanced communication features come at the price of complexity which challenges interoperability across different Web services stack implementations. Interoperability, in turn, is a predominant requirement for an integration technology such as Web services, in particular if inter-organizational business processes are supposed to be implemented on top of that technology. This paper approaches the problem of testing the interoperability of the so-called WS-* standards, advanced Web services communication features that are typically defined as SOAP extensions and configured using WS-Policy. Being essential to business process integration, WS-Security and WS-ReliableMessaging are selected as representatives of this group and the two major Java-based Web services stack implementations Metro and Axis2 are tested for interoperability. We operationalize the notion of interoperability for testing WS-* standards, suppose an approach for deriving test cases from WS-* specifications as well as a method for performing the test cases, and we provide a comprehensive interoperability review of the two selected Web services stack implementations.
Im Servicegeschäft werden Eventkorrelationssysteme verwendet, um bevorstehende Systemzustände vorherzusagen und damit Anlagenausfälle zu vermeiden. Aus strategischer Sicht kann dies zu höherer Anlagenverfügbarkeit und bessere Planbarkeit beitragen. Unternehmen, die von diesem Wettbewerbsvorteil profitieren wollen, begegnen dabei häufig zwei grundlegenden Problemen: die Komplexität im Service steigt mit der Anzahl der Korrelationen. Gleichzeitig geht die Kostentransparenz für den Gesamtprozess verloren. Die vorliegende Arbeit zeigt, warum diese beiden Probleme entstehen und wie sie weitgehend vermieden werden können.
Current software model checkers quickly reach their limit when being applied to verifying pointer safety properties in source code that includes function pointers and inlined assembly. This paper introduces an alternative technique for checking pointer safety violations, called Symbolic Object Code Analysis (SOCA), which is based on bounded symbolic execution, incorporates path-sensitive slicing, and employs the SMT solver Yices as its execution and verification engine. Extensive experimental results of a prototypic SOCA Verifier, using the Verisec suite and almost 10,000 Linux device driver functions as benchmarks, show that SOCA performs competitively to current source-code model checkers and that it also scales well when applied to real operating systems code and pointer safety issues. SOCA effectively explores semantic niches of software that current software verifiers do not reach.
By using the remote functions of a modern IT service management system infrastructure, it is possible to analyze huge amounts of logfile data from complex technical equipment. This enables a service provider to predict failures of connected equipment before they happen. The problem most providers face in this context is finding a needle in a haystack - the obtained amount of data turns out to be too large to be analyzed manually. This report describes a process to find suitable predictive patterns in log files for the detection of upcoming critical situations. The identification process may serve as a hands-on guide. It describes how to connect statistical means, data mining algorithms and expert domain knowledge in the domain of service management. The process was developed in a research project which is currently being carried out within the Siemens Healthcare service organization. The project deals with two main aspects: First, the identification of predictive patterns in existing service data and second, the architecture of an autonomous agent which is able to correlate such patterns. This paper summarizes the results of the first project challenge. The identification process was tested successfully in a proof of concept for several Siemens Healthcare products.