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Performance-oriented DevOps: A Research Agenda

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arxiv 1508.04752 v1 pith:ESGSICMB submitted 2015-08-18 cs.SE cs.PF

classification cs.SEcs.PF
keywords performancedevopsactivitiesduringintegrationconceptsdevelopmentensure
verification ladder T0 review T1 audit T2 compute T3 formal

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DevOps is a trend towards a tighter integration between development (Dev) and operations (Ops) teams. The need for such an integration is driven by the requirement to continuously adapt enterprise applications (EAs) to changes in the business environment. As of today, DevOps concepts have been primarily introduced to ensure a constant flow of features and bug fixes into new releases from a functional perspective. In order to integrate a non-functional perspective into these DevOps concepts this report focuses on tools, activities, and processes to ensure one of the most important quality attributes of a software system, namely performance. Performance describes system properties concerning its timeliness and use of resources. Common metrics are response time, throughput, and resource utilization. Performance goals for EAs are typically defined by setting upper and/or lower bounds for these metrics and specific business transactions. In order to ensure that such performance goals can be met, several activities are required during development and operation of these systems as well as during the transition from Dev to Ops. Activities during development are typically summarized by the term Software Performance Engineering (SPE), whereas activities during operations are called Application Performance Management (APM). SPE and APM were historically tackled independently from each other, but the newly emerging DevOps concepts require and enable a tighter integration between both activity streams. This report presents existing solutions to support this integration as well as open research challenges in this area.

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Cited by 1 Pith paper

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  1. An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning

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    SaFReL uses fuzzy reinforcement learning and a two-phase transfer strategy to generate resource-reduction test cases that reach software performance breaking points more efficiently than random stress testing in a sim...

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