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| 001 | on1046067086 | ||
| 003 | OCoLC | ||
| 005 | 20250707091721.0 | ||
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| 049 | _aMAIN | ||
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_aMachine learning for dynamic software analysis : _bpotentials and limits : International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised papers / _cAmel Bennaceur, Reiner Hähnle, Karl Meinke (eds.). |
| 264 | 1 |
_aCham, Switzerland : _bSpringer, _c2018. |
|
| 300 |
_a1 online resource (ix, 257 pages) : _billustrations |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 | _atext file | ||
| 347 | _bPDF | ||
| 490 | 1 |
_aLecture notes in computer science, _x0302-9743 ; _v11026 |
|
| 490 | 1 | _aLNCS sublibrary. SL 2, Programming and software engineering | |
| 500 | _aIncludes author index. | ||
| 588 | 0 | _aOnline resource; title from PDF title page (SpringerLink, viewed July 26, 2018). | |
| 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 | ||
| 650 | 0 |
_aMachine learning _vCongresses. _915308 |
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| 650 | 6 |
_aApprentissage automatique _vCongrès. _920183 |
|
| 650 | 7 |
_aArtificial intelligence. _2bicssc _91340 |
|
| 650 | 7 |
_aComputer science. _2bicssc _9941 |
|
| 650 | 7 |
_aSoftware Engineering. _2bicssc _914736 |
|
| 650 | 7 |
_aComputers _xIntelligence (AI) & Semantics. _2bisacsh _917680 |
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| 650 | 7 |
_aComputers _xComputer Science. _2bisacsh _917992 |
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| 650 | 7 |
_aComputers _xSoftware Development & Engineering _xGeneral. _2bisacsh _94347 |
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| 650 | 7 |
_aMachine learning _2fast _91680 |
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| 655 | 2 |
_aCongress _911670 |
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| 655 | 7 |
_aproceedings (reports) _2aat |
|
| 655 | 7 |
_aConference papers and proceedings _2fast _96065 |
|
| 655 | 7 |
_aConference papers and proceedings. _2lcgft _96065 |
|
| 655 | 7 |
_aActes de congrès. _2rvmgf _9609890 |
|
| 700 | 1 |
_aBennaceur, Amel, _eeditor. _964507 |
|
| 700 | 1 |
_aHähnle, Reiner, _eeditor. _1https://id.oclc.org/worldcat/entity/E39PBJhMqGBPqXDyqFfpHjwt8C _925349 |
|
| 700 | 1 |
_aMeinke, Karl, _eeditor. _964508 |
|
| 711 | 2 |
_aInternational Dagstuhl Seminar on Machine Learning for Dynamic Software Analysis: Potential and Limits _d(2016 : _cDagstuhl, Wadern, Germany) _964509 |
|
| 758 |
_ihas work: _aMachine learning for dynamic software analysis (Text) _1https://id.oclc.org/worldcat/entity/E39PCGHFR83j3MJkx9cTfM4C73 _4https://id.oclc.org/worldcat/ontology/hasWork |
||
| 776 | 0 | 8 |
_iPrinted edition: _z9783319965611 |
| 830 | 0 |
_aLecture notes in computer science ; _v11026. _x0302-9743 |
|
| 830 | 0 |
_aLNCS sublibrary. _nSL 2, _pProgramming and software engineering. _920654 |
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| 856 | 4 | 0 | _uhttps://link.springer.com/10.1007/978-3-319-96562-8 |
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