02561nam 2200649Ia 450 991077861240332120230524233237.01-280-21255-197866102125520-309-54332-00-585-14307-2(CKB)110986584753360(OCoLC)427404683(CaPaEBR)ebrary10062892(SSID)ssj0000226321(PQKBManifestationID)11198419(PQKBTitleCode)TC0000226321(PQKBWorkID)10258999(PQKB)10578939(MiAaPQ)EBC3376983(Au-PeEL)EBL3376983(CaPaEBR)ebr10062892(OCoLC)923267226(EXLCZ)9911098658475336019900824d1990 uy 0engurcn|||||||||txtccrPrecollege science and mathematics teachers[electronic resource] monitoring supply, demand, and quality /Dorothy M. Gilford and Ellen Tenenbaum, editorsWashington, D.C. National Academy Press19901 online resource (266 p.)"Panel on Statistics on Supply and Demand for Precollege Science and Mathematics Teachers, F. Thomas Juster, chair; Committee on National Statistics, Commission on Behavioral and Social Sciences and Education, National Research Council"--T.p.0-309-04197-X Includes bibliographical references (p. 183-209).Mathematics teachersSupply and demandUnited StatesScience teachersSupply and demandUnited StatesMathematics teachersTraining ofScience teachersTraining ofMathematics teachersSupply and demandScience teachersSupply and demandMathematics teachersTraining of.Science teachersTraining of.331.12/915071/073Juster F. Thomas(Francis Thomas),1926-2010.34143Gilford Dorothy M102734Tenenbaum Ellen1526720National Research Council (U.S.).Panel on Statistics on Supply and Demand for Precollege Science and Mathematics Teachers.National Research Council (U.S.).Committee on National Statistics.MiAaPQMiAaPQMiAaPQBOOK9910778612403321Precollege science and mathematics teachers3768964UNINA03785nam 22006015 450 991034941860332120200705234014.03-319-96562-X10.1007/978-3-319-96562-8(CKB)4100000005323375(DE-He213)978-3-319-96562-8(MiAaPQ)EBC6303373(PPN)22950261X(EXLCZ)99410000000532337520180720d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierMachine Learning for Dynamic Software Analysis: Potentials and Limits International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers /edited by Amel Bennaceur, Reiner Hähnle, Karl Meinke1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (IX, 257 p. 38 illus.) Programming and Software Engineering ;11026Includes index.3-319-96561-1 Introduction -- Testing and Learning -- Extensions of Automata Learning -- Integrative Approaches.Machine 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.Programming and Software Engineering ;11026Software engineeringArtificial intelligenceComputersSoftware Engineering/Programming and Operating Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/I14002Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Theory of Computationhttps://scigraph.springernature.com/ontologies/product-market-codes/I16005Software engineering.Artificial intelligence.Computers.Software Engineering/Programming and Operating Systems.Artificial Intelligence.Theory of Computation.006.31Bennaceur Ameledthttp://id.loc.gov/vocabulary/relators/edtHähnle Reineredthttp://id.loc.gov/vocabulary/relators/edtMeinke Karledthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910349418603321Machine Learning for Dynamic Software Analysis: Potentials and Limits2154584UNINA