000 04113nam a22005415i 4500
001 978-3-642-54157-5
003 DE-He213
005 20200421112546.0
007 cr nn 008mamaa
008 140423s2014 gw | s |||| 0|eng d
020 _a9783642541575
_9978-3-642-54157-5
024 7 _a10.1007/978-3-642-54157-5
_2doi
050 4 _aQA76.758
072 7 _aUMZ
_2bicssc
072 7 _aCOM051230
_2bisacsh
082 0 4 _a005.1
_223
100 1 _aMendes, Emilia.
_eauthor.
245 1 0 _aPractitioner's Knowledge Representation
_h[electronic resource] :
_bA Pathway to Improve Software Effort Estimation /
_cby Emilia Mendes.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXI, 211 p. 84 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1: Introduction to knowledge management -- Chapter 2: Web development vs. software development -- Chapter 3: Introduction to effort estimation -- Chapter 4: Literature review on Web effort estimation -- Chapter 5: Introduction to Bayesian network models -- Chapter 6: Expert-based knowledge engineering of Bayesian networks -- Chapter 7: First case study -- Chapter 8: Second case study -- Chapter 9: Third case study -- Chapter 10: Fourth case study -- Chapter 11: Fifth case study -- Chapter 12: Sixth case study -- Chapter 13: Ways in which to use Bayesian network models within a company -- Chapter 14: Conclusions.
520 _aThe main goal of this book is to help organizations improve their effort estimates and effort estimation processes by providing a step-by-step methodology that takes them through the creation and validation of models that are based on their own knowledge and experience. Such models, once validated, can then be used to obtain predictions, carry out risk analyses, enhance their estimation processes for new projects, and generally advance them as learning organizations. Emilia Mendes presents the Expert-Based Knowledge Engineering of Bayesian Networks (EKEBNs) methodology, which she has used and adapted during the course of several industry collaborations with different companies world-wide over more than 6 years. The book itself consists of two major parts: first, the methodology's foundations in knowledge management, effort estimation (with special emphasis on the intricacies of software and Web development), and Bayesian networks are detailed; then six industry case studies are presented which illustrate the practical use of EKEBNs.  Domain experts from each company participated in the elicitation of the bespoke models for effort estimation, and all models were built employing the widely-used Netica ™ tool. This part is rounded off with a chapter summarizing the experiences with the methodology and the derived models. Practitioners working on software project management, software process quality, or effort estimation and risk analysis in general will find a thorough introduction into an industry-proven methodology as well as numerous experiences, tips and possible pitfalls invaluable for their daily work.
650 0 _aComputer science.
650 0 _aProject management.
650 0 _aKnowledge management.
650 0 _aSoftware engineering.
650 0 _aMathematical statistics.
650 0 _aArtificial intelligence.
650 0 _aManagement information systems.
650 1 4 _aComputer Science.
650 2 4 _aSoftware Engineering.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aProject Management.
650 2 4 _aManagement of Computing and Information Systems.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aKnowledge Management.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642541568
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-54157-5
912 _aZDB-2-SCS
942 _cEBK
999 _c58583
_d58583