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245 1 0 _aMachine Learning for Dynamic Software Analysis: Potentials and Limits
_h[electronic resource] :
_bInternational Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers /
_cedited by Amel Bennaceur, Reiner Hähnle, Karl Meinke.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aIX, 257 p. 38 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aProgramming and Software Engineering ;
_v11026
500 _aAcceso multiusuario
505 0 _aIntroduction -- Testing and Learning -- Extensions of Automata Learning -- Integrative Approaches.
520 _aMachine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits" held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aSoftware engineering.
650 0 _aArtificial intelligence.
650 0 _aComputers.
650 1 4 _aSoftware Engineering/Programming and Operating Systems.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I14002
650 2 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _aTheory of Computation.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I16005
700 1 _aBennaceur, Amel.
_eeditor.
_0(orcid)0000-0002-6124-9622
_1https://orcid.org/0000-0002-6124-9622
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aHähnle, Reiner.
_eeditor.
_0(orcid)0000-0001-8000-7613
_1https://orcid.org/0000-0001-8000-7613
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMeinke, Karl.
_eeditor.
_0(orcid)0000-0002-9706-5008
_1https://orcid.org/0000-0002-9706-5008
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319965611
776 0 8 _iPrinted edition:
_z9783319965635
830 0 _aProgramming and Software Engineering ;
_v11026
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-96562-8
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912 _aZDB-2-SXCS
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