000			04447cam a2200493Ii 4500
001			ocn953844182
003			OCoLC
005			20190328114815.0
006			m o d
007			cr cnu---unuuu
008			160721s2016 maua ob 000 0 eng d
040			_aYDXCP _beng _erda _epn _cYDXCP _dOPELS _dUIU _dEBLCP _dN$T _dUMI _dIDEBK _dOCLCQ _dN$T _dOCLCF _dUPM _dTOH _dSTF _dCOO _dNAM _dDEBBG _dS9I _dOCLCQ
019			_a953849331 _a957279026
020			_a9780128042618 _q(electronic bk.)
020			_a0128042613 _q(electronic bk.)
020			_z0128042060
020			_z9780128042069
035			_a(OCoLC)953844182 _z(OCoLC)953849331 _z(OCoLC)957279026
050		4	_aQA76.758 _b.P47 2016
072		7	_aCOM _x051000 _2bisacsh
082	0	4	_a005.1 _223
245	0	0	_aPerspectives on data science for software engineering / _h[electronic resource] _cedited by Tim Menzies, Laurie Williams, Thomas Zimmermann.
264		1	_aCambridge, MA : _bMorgan Kaufmann is an imprint of Elsevier, _c2016.
300			_a1 online resource (xxix, 378 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aOnline resource; title from PDF title page (ScienceDirect, viewed Aug. 1, 2016).
505	0		_aFront Cover; Perspectives on Data Science for Software Engineering; Copyright; Contents; Contributors; Acknowledgments; Introduction; Perspectives on data science for software engineering; Why This Book?; About This Book; The Future; References; Software analytics and its application in practice; Six Perspectives of Software Analytics; Experiences in Putting Software Analytics into Practice; References; Seven principles of inductive software engineering: What we do is different; Different and Important; Principle #1: Humans Before Algorithms; Principle #2: Plan for Scale.
505	8		_aPrinciple #3: Get Early FeedbackPrinciple #4: Be Open Minded; Principle #5: Be smart with your learning; Principle #6: Live With the Data You Have; Principle #7: Develop a Broad Skill Set That Uses a Big Toolkit; References; The need for data analysis patterns (in software engineering); The Remedy Metaphor; Software Engineering Data; Needs of Data Analysis Patterns; Building Remedies for Data Analysis in Software Engineering Research; References; From software data to software theory: The path less traveled; Pathways of Software Repository Research; From Observation, to Theory, to Practice.
505	8		_aDynamic Artifacts Are Here to StayAcknowledgments; References; Mobile app store analytics; Introduction; Understanding End Users; Conclusion; References; The naturalness of software*; Introduction; Transforming Software Practice; Porting and Translation; The ``Natural Linguistics�� of Code; Analysis and Tools; Assistive Technologies; Conclusion; References; Advances in release readiness; Predictive Test Metrics; Universal Release Criteria Model; Best Estimation Technique; Resource/Schedule/Content Model; Using Models in Release Management.
505	8		_aResearch to Implementation: A Difficult (but Rewarding) JourneyHow to tame your online services; Background; Service Analysis Studio; Success Story; References; Measuring individual productivity; No Single and Simple Best Metric for Success/Productivity; Measure the Process, Not Just the Outcome; Allow for Measures to Evolve; Goodharts Law and the Effect of Measuring; How to Measure Individual Productivity?; References; Stack traces reveal attack surfaces; Another Use of Stack Traces?; Attack Surface Approximation; References; Visual analytics for software engineering data; References.
504			_aIncludes bibliographical references.
520			_aPresenting the best practices of seasoned data miners in software engineering, this book offers unique insights into the wisdom of the community{OCLCbr#92}s leaders gathered to share hard-won lessons from the trenches. -- _cEdited summary from book.
650		7	_aCOMPUTERS / General. _2bisacsh
650		0	_aSoftware engineering.
650		7	_aSoftware engineering. _2fast _0(OCoLC)fst01124185
655		4	_aElectronic books.
700	1		_aMenzies, Tim, _eeditor.
700	1		_aWilliams, Laurie, _d1962- _eeditor.
700	1		_aZimmermann, Thomas, _eeditor.
776	0	8	_iPrint version: _z0128042060 _z9780128042069 _w(OCoLC)926742865
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128042069
999			_c247381 _d247381