Modeling a Realistic Workload for Performance Testing

Lutteroth, Christof; Weber, Gerald

doi:10.1109/edoc.2008.40

Cited by 23 publications

(11 citation statements)

References 28 publications

(28 reference statements)

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“…For example, a user login action can either be a successful login and a redirect to the overview page, or a failure login and a redirect back to the login page. Such user behavior is difficult to model in a Markov chain [106], [107].…”

Section: ) Testing Loads Derived Using Stochastic Formoriented Modelsmentioning

confidence: 99%

A Survey on Load Testing of Large-Scale Software Systems

Jiang

Hassan²

2015

IIEEE Trans. Software Eng.

145

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Many large-scale software systems must service thousands or millions of concurrent requests. These systems must be load tested to ensure that they can function correctly under load (i.e., the rate of the incoming requests). In this paper, we survey the state of load testing research and practice. We compare and contrast current techniques that are used in the three phases of a load test: (1) designing a proper load, (2) executing a load test, and (3) analyzing the results of a load test. This survey will be useful for load testing practitioners and software engineering researchers with interest in the load testing of large-scale software systems.

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Section: ) Testing Loads Derived Using Stochastic Formoriented Modelsmentioning

confidence: 99%

A Survey on Load Testing of Large-Scale Software Systems

Jiang

Hassan²

2015

IIEEE Trans. Software Eng.

145

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“…A different set of approaches models input data separately from the request model . However, the provided input data models are specific to the respective request models.…”

Section: Related Workmentioning

confidence: 99%

Reducing the maintenance effort for parameterization of representative load tests using annotations

Schulz¹,

Hoorn

Wert³

2019

Software Testing Verif & Rel

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Summary Directly affecting the user experience, performance is a crucial aspect of today's software applications. Representative load testing allows to effectively test and preserve the performance before delivery by mimicking the actually expected workload. In the literature, various approaches have been proposed for extracting representative load tests from recorded user sessions. However, these approaches require manual parameterization for specifying input data and adjusting static properties such as a request's domain name. This manual effort accumulates when load tests need to be updated due to changing production workloads and APIs. In this paper, we address the reduction of the maintenance effort for representative load testing. We introduce input data and properties annotations (IDPAs) that store manual parameterizations and can be evolved automatically. Experts only have to parameterize extracted load tests initially. For dealing with API changes, we develop approaches to evolve IDPAs for the types of changes described in the literature. We evaluated our approach in two experimental studies, by deriving effort estimation models, and in an industrial case study including four different software projects. Our evaluation shows that IDPAs can parameterize generated load tests for restoring the representativeness, especially for applications with workloads dominated by request orders and rates. The maintenance effort can be reduced from a quadratic cumulative effort over time to a linear cumulative effort for a typical mix of API changes. Furthermore, we were able to express all parameterizations required by the industrial projects using the IDPA but also had to integrate extensions using the provided extension mechanisms.

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“…Lutteroth and Weber [LW08], from the University of Auckland in New Zealand, present a study to model realistic workload for performance testing. They argue that predictive models such as stochastic models are easier to represent, besides allowing greater coverage of the different system iteration flows.…”

Section: "Research Is What I'm Doing When I Don't Know What I'm Doingmentioning

confidence: 99%