LEADER 02924nam 2200553 450 001 996426336503316 005 20200707020548.0 010 $a0-12-411543-8 035 $a(CKB)3710000000470945 035 $a(EBL)4003128 035 $a(SSID)ssj0001587070 035 $a(PQKBManifestationID)16270016 035 $a(PQKBTitleCode)TC0001587070 035 $a(PQKBWorkID)14869464 035 $a(PQKB)11334644 035 $a(MiAaPQ)EBC4003128 035 $a(CaSebORM)9780124115439 035 $a(PPN)233403590 035 $a(EXLCZ)993710000000470945 100 $a20151127h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe art and science of analyzing software data /$fChristian Bird, Tim Menzies, Thomas Zimmermann ; contributors, Alberto Bacchelli [and seventy-two others] 205 $aFirst edition. 210 1$aAmsterdam, Netherlands :$cElsevier,$d2015. 210 4$dİ2015 215 $a1 online resource (0 p.) 300 $aDescription based upon print version of record. 311 $a0-12-411519-5 320 $aIncludes bibliographical references at the end of each chapters and index. 330 $aThe Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions. Presents best practices, hints, and tips to analyze data and apply tools in data science projects Presents research methods and case studies that have emerged over the past few years to further understanding of software data Shares stories from the trenches of successful data science initiatives in industry 606 $aData mining 606 $aComputer programming$xManagement 608 $aElectronic books. 615 0$aData mining. 615 0$aComputer programming$xManagement. 676 $a006.312 700 $aBird$b Christian$0968989 702 $aMenzies$b Tim 702 $aZimmermann$b Thomas 702 $aBacchelli$b Alberto 801 0$bMiAaPQ 906 $aBOOK 912 $a996426336503316 996 $aThe art and science of analyzing software data$92201408 997 $aUNISA