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  1. Proceedings of the 2nd International Workshop on Quality-Aware DevOps (QUDOS 2016)
  2. PET: continuous performance evaluation tool
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Coverage-based metrics for cloud adaptation
DICE fault injection tool
Management challenges for DevOps adoption within UK SMEs
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A software architecture framework for quality-aware DevOps
Model-driven continuous deployment for quality DevOps
Towards a UML profile for data intensive applications
PET: continuous performance evaluation tool
A systematic approach for performance evaluation using process mining: the POSIDONIA operations case study
A tool for verification of big-data applications
The M3 (measure-measure-model) tool-chain for performance prediction of multi-tier applications
TemPerf: temporal correlation between performance metrics and source code

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PET: continuous performance evaluation tool

Content Provider ACM Digital Library
Author Willnecker, Felix Kroß, Johannes Zwickl, Thomas Krcmar, Helmut
Abstract Performance measurements and simulations produce large amounts of data in a short period of time. Release cycles are getting shorter due to the DevOps movement and heavily rely on live data from production or test environments. In addition, performance simulations increasingly become accurate and close to exact predictions. Results from these simulations are reliable and can be compared with live data to detect deviations from expected behavior. In this work, we present a comprehensive tool that can process and analyze measurement as well as simulation data quickly utilizing big data technologies. Live measurement data and simulation results can be analyzed for detecting performance problems, deviations from expected behavior or to simply compare a performance model with real world applications. .
Starting Page 42
Ending Page 43
Page Count 2
File Format PDF
ISBN 9781450344111
DOI 10.1145/2945408.2945418
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-07-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Performance evaluation Performance analysis
Content Type Text
Resource Type Article
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