LEADER 03810nam 22006015 450 001 996466190603316 005 20200705234014.0 010 $a3-319-96562-X 024 7 $a10.1007/978-3-319-96562-8 035 $a(CKB)4100000005323375 035 $a(DE-He213)978-3-319-96562-8 035 $a(MiAaPQ)EBC6303373 035 $a(PPN)22950261X 035 $a(EXLCZ)994100000005323375 100 $a20180720d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Dynamic Software Analysis: Potentials and Limits$b[electronic resource] $eInternational Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers /$fedited by Amel Bennaceur, Reiner Hähnle, Karl Meinke 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (IX, 257 p. 38 illus.) 225 1 $aProgramming and Software Engineering ;$v11026 300 $aIncludes index. 311 $a3-319-96561-1 327 $aIntroduction -- Testing and Learning -- Extensions of Automata Learning -- Integrative Approaches. 330 $aMachine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits? held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches. 410 0$aProgramming and Software Engineering ;$v11026 606 $aSoftware engineering 606 $aArtificial intelligence 606 $aComputers 606 $aSoftware Engineering/Programming and Operating Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I14002 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aTheory of Computation$3https://scigraph.springernature.com/ontologies/product-market-codes/I16005 615 0$aSoftware engineering. 615 0$aArtificial intelligence. 615 0$aComputers. 615 14$aSoftware Engineering/Programming and Operating Systems. 615 24$aArtificial Intelligence. 615 24$aTheory of Computation. 676 $a006.31 702 $aBennaceur$b Amel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHähnle$b Reiner$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMeinke$b Karl$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466190603316 996 $aMachine Learning for Dynamic Software Analysis: Potentials and Limits$92154584 997 $aUNISA