TY - BOOK ID - 704479 TI - Practitioner's Knowledge Representation : A Pathway to Improve Software Effort Estimation PY - 2014 SN - 3642541577 3642541569 PB - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, DB - UniCat KW - Knowledge representation (Information theory) KW - Data processing. KW - Representation of knowledge (Information theory) KW - Artificial intelligence KW - Information theory KW - Software engineering. KW - Computer science. KW - Project management. KW - Information Systems. KW - Artificial intelligence. KW - Knowledge management. KW - Software Engineering. KW - Probability and Statistics in Computer Science. KW - Project Management. KW - Management of Computing and Information Systems. KW - Artificial Intelligence. KW - Knowledge Management. KW - Management of knowledge assets KW - Management KW - Information technology KW - Intellectual capital KW - Organizational learning KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Industrial project management KW - Informatics KW - Science KW - Computer software engineering KW - Engineering KW - Mathematical statistics. KW - Management information systems. KW - Computer-based information systems KW - EIS (Information systems) KW - Executive information systems KW - MIS (Information systems) KW - Sociotechnical systems KW - Information resources management KW - Mathematics KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Probabilities KW - Sampling (Statistics) KW - Communication systems KW - Statistical methods UR - https://www.unicat.be/uniCat?func=search&query=sysid:704479 AB - The 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. ER -