HAPPE, Lucia, Barbora BÜHNOVÁ and Ralf REUSSNER. Stateful component-based performance models. Software & Systems Modeling. Springer, 2014, vol. 13, No 4, p. 1319-1343. ISSN 1619-1366. Available from: https://dx.doi.org/10.1007/s10270-013-0336-6.
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Basic information
Original name Stateful component-based performance models
Authors HAPPE, Lucia (703 Slovakia), Barbora BÜHNOVÁ (203 Czech Republic, guarantor, belonging to the institution) and Ralf REUSSNER (276 Germany).
Edition Software & Systems Modeling, Springer, 2014, 1619-1366.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
WWW Springer link
Impact factor Impact factor: 1.408
RIV identification code RIV/00216224:14330/14:00074685
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1007/s10270-013-0336-6
UT WoS 000342493300007
Keywords in English Stateful components; performance prediction; prediction accuracy
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 27/4/2015 03:08.
Abstract
The accuracy of performance-prediction models is crucial for widespread adoption of performance prediction in industry. One of the essential accuracy-influencing aspects of software systems is the dependence of system behaviour on a configuration, context or history related state of the system, typically reflected with a (persistent) system attribute. Even in the domain of component-based software engineering, the presence of state-reflecting attributes (the so-called internal states) is a natural ingredient of the systems, implying the existence of stateful services, stateful components and stateful systems as such. Currently, there is no consensus on the definition or method to include state-related information in component-based prediction models. Besides the task to identify and localise different types of stateful information across component-based software architecture, the issue is to balance the expressiveness and complexity of prediction models via an effective abstraction of state modelling. In this paper, we identify and classify stateful information in component-based software systems, study the performance impact of the individual state categories, and discuss the costs of their modelling in terms of the increased model size. The observations are formulated into a set of heuristics-guiding software engineers in state modelling. Finally, practical effect of state modelling on software performance is evaluated on a real-world case study, the SPECjms2007 Benchmark. The observed deviation of measurements and predictions was significantly decreased by more precise models of stateful dependencies.
Links
LG13010, research and development projectName: Zastoupení ČR v European Research Consortium for Informatics and Mathematics (Acronym: ERCIM-CZ)
Investor: Ministry of Education, Youth and Sports of the CR
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