Very large volumes of data are collected and stored at the organizational level; software algorithms and dedicated products require this data to be shared. There is a great interest in addressing the data sharing models using data mining solutions. These solutions allow the discovery of patterns within data by using predictive techniques. The models play an important role in making decisions because they highlight areas where the ongoing processes within the organization require improvement.

This book focuses on two fundamental issues: data sharing and sharing models. Another contribution to the success of this book is the way in which the authors gradually structure the information: the first two parts mainly address practitioners, while the other two parts address specialists. The book will also serve researchers, as well as managers and technical developers.

Organizational issues and a number of technical topics contribute to the success of a data mining project. In Part 1 (“Data Mining for Managers”), some specific rules regarding industrial data science and data mining research are briefly stated and carefully analyzed: “Talk to the users”; “Know the domain”; “Suspect your data”; and “Data science is cyclic.”

Next, “Data Mining: A Technical Tutorial,” includes “Data Mining and Software Engineering”; “Defect Prediction”; “Effort Estimation”; and “Data Mining (Under the Hood),” where the authors approach the application areas and details of data mining in software engineering. The authors address readers who are less informed about data mining.

Part 3, “Sharing Data,” includes “Sharing Data: Challenges and Methods”; “Learning Contexts”; “Cross-Company Learning: Handling the Data Drought”; “Building Smarter Transfer Learners”; “Sharing Less Data (Is a Good Thing)”; “How to Keep Your Data Private”; “Compensating for Missing Data”; and “Active Learning: Learning More with Less.” This part addresses the ways and the situations in which, within the area of software engineering, the relevant data can be used for other projects. Modern data mining solutions are analyzed, and the authors have richly illustrated that their purpose is to fill in missing data and shared data security.

Finally, “Sharing Models” includes “Sharing Models: Challenges and Methods”; “How to Adapt Models in a Dynamic World”; “Ensembles of Learning Machines”; “Complexity: Using Assemblies of Multiple Models”; “The Importance of Goals in Model-Based Reasoning”; and “Using Goals in Model-Based Reasoning.” This part shows the way in which software engineering models can be shared between projects.

A result of laborious documentation, this book is well founded. The solutions and personal contributions from the authors lead me to recommend this book to both academics and researchers. Meanwhile, young researchers, managers, and anyone interested in the problem of sharing data within the software engineering area will find in this book a real opportunity to learn about the topic.