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 qualityor 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."
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