LEADER 03660nam 2200589 450 001 9910826832303321 005 20200520144314.0 010 $a0-12-417307-1 035 $a(CKB)3710000000324202 035 $a(SSID)ssj0001436025 035 $a(PQKBManifestationID)11817153 035 $a(PQKBTitleCode)TC0001436025 035 $a(PQKBWorkID)11434534 035 $a(PQKB)10080337 035 $a(Au-PeEL)EBL1910044 035 $a(CaPaEBR)ebr11001150 035 $a(CaONFJC)MIL732171 035 $a(OCoLC)896901265 035 $a(CaSebORM)9780124172951 035 $a(MiAaPQ)EBC1910044 035 $a(PPN)194314340 035 $a(EXLCZ)993710000000324202 100 $a20150117h20152015 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSharing data and models in software engineering /$fTim Menzies [and four others] ; designer, Mark Rogers 205 $aFirst edition. 210 1$aWaltham, Massachusetts :$cMorgan Kaufmann,$d2015. 210 4$dİ2015 215 $a1 online resource (415 pages) $cillustrations (some color), graphs 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-336-00889-X 311 $a0-12-417295-4 320 $aIncludes bibliographical references and indexes. 330 $aData Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects. Shares the specific experience of leading researchers and techniques developed to handle data problems in the realm of software engineering Explains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfalls Provides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge research Addresses current challenges with software engineering data such as lack of local data, access issues due to data privacy, increasing data quality via cleaning of spurious chunks in data 606 $aSoftware engineering 606 $aComputer-aided software engineering 615 0$aSoftware engineering. 615 0$aComputer-aided software engineering. 676 $a005.1 702 $aMenzies$b Tim 702 $aRogers$b Mark 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826832303321 996 $aSharing data and models in software engineering$93946158 997 $aUNINA