sas 15(3): e5

Research Article

Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems

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  • @ARTICLE{10.4108/icst.valuetools.2014.258171,
        author={Andre van Hoorn and Christian V\o{}gele and Eike Schulz and Wilhelm Hasselbring and Helmut Krcmar},
        title={Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems},
        journal={EAI Endorsed Transactions on Self-Adaptive Systems},
        volume={1},
        number={3},
        publisher={EAI},
        journal_a={SAS},
        year={2015},
        month={2},
        keywords={workload specifications, load testing, clustering, session-based application systems, load test extraction},
        doi={10.4108/icst.valuetools.2014.258171}
    }
    
  • Andre van Hoorn
    Christian Vögele
    Eike Schulz
    Wilhelm Hasselbring
    Helmut Krcmar
    Year: 2015
    Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems
    SAS
    EAI
    DOI: 10.4108/icst.valuetools.2014.258171
Andre van Hoorn1, Christian Vögele2,*, Eike Schulz3, Wilhelm Hasselbring3, Helmut Krcmar4
  • 1: University of Stuttgart
  • 2: fortiss GmbH
  • 3: Kiel University
  • 4: Technische Universität München
*Contact email: voegele@fortiss.org

Abstract

Workload generation is essential to systematically evaluate performance properties of application systems under controlled conditions, e.g., in load tests or benchmarks. The definition of workload specifications that represent the real workload as accurately as possible is one of the biggest challenges in this area. This paper presents our approach for the modeling and automatic extraction of probabilistic workload specifications for load testing session-based application systems. The approach, called WESSBAS, comprises (i.) a domain specific language (DSL) enabling layered modeling of workload specifications as well as support for (ii.) automatically extracting instances of the DSL from recorded sessions logs and (iii.) transforming instances of the DSL to workload specifications of existing load testing tools. During the extraction process, different groups of customers with similar navigational patterns are identified using clustering techniques. We developed corresponding tool support including a transformation to probabilistic test scripts for the Apache JMeter load testing tool. The evaluation of the proposed approach using the industry standard benchmark SPECjEnterprise2010 demonstrates its applicability and the representativeness of the extracted workloads.