Practitioner's knowledge representation

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The primary aim of this book is to enhance organizations' effort estimation processes by providing a detailed methodology for creating and validating models based on their own expertise. Once validated, these models can be utilized for predictions, risk analyses, and improving estimation processes for new projects, fostering a culture of learning within the organization. Emilia Mendes introduces the Expert-Based Knowledge Engineering of Bayesian Networks (EKEBNs) methodology, refined through over six years of collaboration with various companies globally. The book is divided into two main sections: the first outlines the methodology's foundations in knowledge management, effort estimation—particularly in software and Web development—and Bayesian networks; the second presents six industry case studies demonstrating the practical application of EKEBNs. Domain experts contributed to the development of tailored effort estimation models, all constructed using the widely-used Netica™ tool. The book concludes with a chapter summarizing the methodology's experiences and the models derived. Practitioners in software project management, process quality, and effort estimation will find a comprehensive introduction to this proven methodology, along with valuable insights, tips, and potential challenges relevant to their work.