Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

57.066 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament d'Enginyeria de Serveis i Sistemes d'Informació
  • Articles de revista
  • View Item
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament d'Enginyeria de Serveis i Sistemes d'Informació
  • Articles de revista
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Data science: technologies for better software

Thumbnail
View/Open
ebert2019data-science.pdf (408,5Kb)
Share:
 
 
10.1109/MS.2019.2933681
 
  View Usage Statistics
Cita com:
hdl:2117/177605

Show full item record
Ebert, Christof
Heidrich, Jens
Martínez Fernández, Silverio JuanMés informacióMés informacióMés informació
Trendowicz, Adam
Document typeArticle
Defense date2019-11
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Data science is mandatory in today's business to capitalize on achievements and assets. This specifically holds for modern software development, where data science facilitates analyzing product, process, and usage and thus managing evolution and performance. With the convergence of embedded and IT domains, such as the Internet of Things (IoT) and automotive systems, software systems are becoming more complex. Complexity has two faces. On one hand it means more functionality and fluid delivery models, thus offering markets more value, such as the ability to deliver a single-customer focus. Complexity, however, also means the growth of technical debt, which slows productivity and lowers quality. As software engineering generates ever larger and more varied data sets, such as feature usage, code analysis, test coverage, error logs, and maintenance data, companies face the challenge of unlocking the value of that data.
CitationEbert, C. [et al.]. Data science: technologies for better software. "IEEE software", Novembre 2019, vol. 36, núm. 6, p. 66-72. 
URIhttp://hdl.handle.net/2117/177605
DOI10.1109/MS.2019.2933681
ISSN0740-7459
Publisher versionhttps://ieeexplore.ieee.org/document/8880036
Other identifiershttp://www.essi.upc.edu/~smartinez/wp-content/papercite-data/pdf/ebert2019data-science.pdf
Collections
  • Departament d'Enginyeria de Serveis i Sistemes d'Informació - Articles de revista [189]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
ebert2019data-science.pdf408,5KbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Inici de la pàgina