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Quality Assessment in DevOps: Automated Analysis of a Tax Fraud Detection System
University of Zaragoza, Spain.ORCID iD: 0000-0002-2736-845x
Netfective Technology, France.
University of Zaragoza, Spain.
2017 (English)In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, New York, USA: ACM Press, 2017, p. 133-138Conference paper, Published paper (Refereed)
Abstract [en]

The paper presents an industrial application of a DevOps process for a Tax fraud detection system. In particular, we report the influence of the quality assessment during development iterations, with special focus on the fulfillment of performance requirements. We investigated how to guarantee quality requirements in a process iteration while new functionalities are added. The experience has been carried out by practitioners and academics in the context of a project for improving quality of data intensive applications.

Place, publisher, year, edition, pages
New York, USA: ACM Press, 2017. p. 133-138
Keywords [en]
DevOps, Model-based QoS, Tax Fraud detection, Data Intensive Applications, Unified Modeling Language
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-74484DOI: 10.1145/3053600.3053632ISBN: 978-1-4503-4899-7 (electronic)OAI: oai:DiVA.org:lnu-74484DiVA, id: diva2:1209364
Conference
3rd International Workshop on Quality-Aware DevOps, L'Aquila, Italy, April 22 - 26, 2017
Projects
H2020 DICE
Funder
EU, Horizon 2020, 644869Available from: 2018-05-22 Created: 2018-05-22 Last updated: 2018-05-23Bibliographically approved

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Publisher's full texthttps://dl.acm.org/citation.cfm?doid=3053600.3053632

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Perez-Palacin, Diego

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf