Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH scholarly article en Bennaceur, Amel; Giannakopoulou, Dimitra; Hähnle, Reiner; Meinke, Karl http://www.dagstuhl.de/dagrep License
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URN: urn:nbn:de:0030-drops-66954
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Weitere Beteiligte (Hrsg. etc.): Amel Bennaceur and Dimitra Giannakopoulou and Reiner Hähnle and Karl Meinke

Machine Learning for Dynamic Software Analysis: Potentials and Limits (Dagstuhl Seminar 16172)

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Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 16172 "Machine Learning for Dynamic Software Analysis: Potentials and Limits". Machine learning is a powerful paradigm for software analysis that provides novel approaches to automating the generation of models and other essential artefacts. This Dagstuhl Seminar brought together top researchers active in the fields of machine learning and software analysis to have a better understanding of the synergies between these fields and suggest new directions and collaborations for future research.

BibTeX - Entry

@Article{bennaceur_et_al:DR:2016:6695,
  author =	{Amel Bennaceur and Dimitra Giannakopoulou and Reiner H{\"a}hnle and Karl Meinke},
  title =	{{Machine Learning for Dynamic Software Analysis: Potentials and Limits (Dagstuhl Seminar 16172)}},
  pages =	{161--173},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{4},
  editor =	{Amel Bennaceur and Dimitra Giannakopoulou and Reiner H{\"a}hnle and Karl Meinke},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2016/6695},
  URN =		{urn:nbn:de:0030-drops-66954},
  doi =		{10.4230/DagRep.6.4.161},
  annote =	{Keywords: Machine learning, Automata learning, Software analysis, Dynamic analysis, Testing, Model extraction, Systems integration}
}

Keywords: Machine learning, Automata learning, Software analysis, Dynamic analysis, Testing, Model extraction, Systems integration
Seminar: Dagstuhl Reports, Volume 6, Issue 4
Issue date: 2016
Date of publication: 2016


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