Preemptive regression test scheduling strategies: a new testing approach to thriving on the volatile service environments

A workflow-based web service may use ultra-late binding to invoke external web services to concretize its implementation at run time. Nonetheless, such external services or the availability of recently used external services may evolve without prior notification, dynamically triggering the workflow-based service to bind to new replacement external services to continue the current execution. Any integration mismatch may cause a failure. In this paper, we propose Preemptive Regression Testing (PRT), a novel testing approach that addresses this adaptive issue. Whenever such a late-change on the service under regression test is detected, PRT preempts the currently executed regression test suite, searches for additional test cases as fixes, runs these fixes, and then resumes the execution of the regression test suite from the preemption point. © 2012 IEEE |postprin

Adaptive Test-Case Prioritization Guided by Output Inspection

Test-case prioritization is to schedule the execution order of test cases so as to maximize some objective (e.g., revealing faults early). The existing test-case prioritization approaches separate the process of test-case prioritization and the process of test-case execution by presenting the execution order of all test cases before programmers start running test cases. As the execution information of the modified program is not available for the existing test-case prioritization approaches, these approaches mainly rely on only the execution information of the previous program before modification. To address this problem, we present an adaptive test-case prioritization approach, which determines the execution order of test cases simultaneously during the execution of test cases. In particular, the adaptive approach selects test cases based on their fault-detection capability, which is calculated based on the output of selected test cases. As soon as a test case is selected and runs, the fault-detection capability of each unselected test case is modified according to the output of the latest selected test case. To evaluate the effectiveness of the proposed adaptive approach, we conducted an experimental study on eight C programs and four Java programs. The experimental results show that the adaptive approach is usually significantly better than the total test-case prioritization approach and competitive to the additional test-case prioritization approach. Moreover, the adaptive approach is better than the additional approach on some subjects (e.g, replace and schedule).http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000331216500026&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Software EngineeringComputer Science, Theory & MethodsEICPCI-S(ISTP)

A subsumption hierarchy of test case prioritization for composite services

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Preemptive regression testing of workflow-based web services

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Enhancing coverage adequacy of service compositions after runtime adaptation

Laufzeitüberwachung (engl. runtime monitoring) ist eine wichtige Qualitätssicherungs-Technik für selbstadaptive Service-Komposition. Laufzeitüberwachung überwacht den Betrieb der Service-Komposition. Zur Bestimmung der Genauigkeit von Software-Tests werden häufig Überdeckungskriterien verwendet. Überdeckungskriterien definieren Anforderungen die Software-Tests erfüllen muss. Wegen ihrer wichtigen Rolle im Software-Testen haben Forscher Überdeckungskriterien an die Laufzeitüberwachung von Service-Komposition angepasst. Die passive Art der Laufzeitüberwachung und die adaptive Art der Service-Komposition können die Genauigkeit von Software-Tests zur Laufzeit negativ beeinflussen. Dies kann jedoch die Zuversicht in der Qualität der Service-Komposition begrenzen. Um die Überdeckung selbstadaptiver Service-Komposition zur Laufzeit zu verbessern, untersucht diese Arbeit, wie die Laufzeitüberwachung und Online-Testen kombiniert werden können. Online-Testen bedeutet dass Testen parallel zu der Verwendung einer Service-Komposition erfolgt. Zunächst stellen wir einen Ansatz vor, um gültige Execution-Traces für Service-Komposition zur Laufzeit zu bestimmen. Der Ansatz berücksichtigt die Execution-Traces von Laufzeitüberwachung und (Online)-Testen. Er berücksichtigt Änderungen im Workflow und Software-Services eines Service-Komposition. Zweitens, definieren wir Überdeckungskriterien für Service-Komposition. Die Überdeckungskriterien berücksichtigen Ausführungspläne einer Service-Komposition und berücksichtigen die Überdeckung für Software-Services und die Service-Komposition. Drittens stellen wir Online-Testfälle Priorisierungs Techniken, um die Abdeckungniveau einer Service-Komposition schneller zu erreichen. Die Techniken berücksichtigen die Überdeckung einer Service-Komposition durch beide Laufzeitüberwachung und Online-Tests. Zusätzlich, berücksichtigen sie die Ausführungszeit von Testfällen und das Nutzungsmodell der Service-Komposition. Viertens stellen wir einen Rahmen für die Laufzeitüberwachung und Online-Testen von Software-Services und Service-Komposition, genannt PROSA, vor. PROSA bietet technische Unterstützung für die oben genannten Beiträge. Wir evaluieren die Beiträge anhand einer beispielhaften Service-Komposition, die häufig in dem Forschungsgebiet Service-oriented Computing eingesetzt wird.Runtime monitoring (or monitoring for short) is a key quality assurance technique for self-adaptive service compositions. Monitoring passively observes the runtime behaviour of service compositions. Coverage criteria are extensively used for assessing the adequacy (or thoroughness) of software testing. Coverage criteria specify certain requirements on software testing. The importance of coverage criteria in software testing has motivated researchers to adapt them to the monitoring of service composition. However, the passive nature of monitoring and the adaptive nature of service composition could negatively influence the adequacy of monitoring, thereby limiting the confidence in the quality of the service composition. To enhance coverage adequacy of self-adaptive service compositions at runtime, this thesis investigates how to combine runtime monitoring and online testing. Online testing means testing a service composition in parallel to its actual usage and operation. First, we introduce an approach for determining valid execution traces for service compositions at runtime. The approach considers execution traces of both monitoring and (online) testing. It considers modifications in both workflow and constituent services of a service composition. Second, we define coverage criteria for service compositions. The criteria consider execution plans of a service composition for coverage assessment and consider the coverage of an abstract service and the overall service composition. Third, we introduce online-test-case prioritization techniques to achieve a faster coverage of a service composition. The techniques employ coverage of a service composition from both monitoring and online testing, execution time of test cases, and the usage model of the service composition. Fourth, we introduce a framework for monitoring and online testing of services and service compositions called PROSA. PROSA provides technical support for the aforementioned contributions. We evaluate the contributions of this thesis using service compositions frequently used in service-oriented computing research

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

Automated Realistic Test Input Generation and Cost Reduction in Service-centric System Testing

Service-centric System Testing (ScST) is more challenging than testing traditional software due to the complexity of service technologies and the limitations that are imposed by the SOA environment. One of the most important problems in ScST is the problem of realistic test data generation. Realistic test data is often generated manually or using an existing source, thus it is hard to automate and laborious to generate. One of the limitations that makes ScST challenging is the cost associated with invoking services during testing process. This thesis aims to provide solutions to the aforementioned problems, automated realistic input generation and cost reduction in ScST. To address automation in realistic test data generation, the concept of Service-centric Test Data Generation (ScTDG) is presented, in which existing services used as realistic data sources. ScTDG minimises the need for tester input and dependence on existing data sources by automatically generating service compositions that can generate the required test data. In experimental analysis, our approach achieved between 93% and 100% success rates in generating realistic data while state-of-the-art automated test data generation achieved only between 2% and 34%. The thesis addresses cost concerns at test data generation level by enabling data source selection in ScTDG. Source selection in ScTDG has many dimensions such as cost, reliability and availability. This thesis formulates this problem as an optimisation problem and presents a multi-objective characterisation of service selection in ScTDG, aiming to reduce the cost of test data generation. A cost-aware pareto optimal test suite minimisation approach addressing testing cost concerns during test execution is also presented. The approach adapts traditional multi-objective minimisation approaches to ScST domain by formulating ScST concerns, such as invocation cost and test case reliability. In experimental analysis, the approach achieved reductions between 69% and 98.6% in monetary cost of service invocations during testin