With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data.

This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: "Part I - Techniques" introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow."Part II - Evaluation" summarizes methods and experimental designs for evaluating recommendations in software engineering."Part III - Applications" describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering.

The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered."

With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: "Part I - Techniques" introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. "Part II - Evaluation" summarizes methods and experimental designs for evaluating recommendations in software engineering. "Part III - Applications" describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered.

This textbook provides an in-depth introduction to software design, with a focus on object-oriented design, and using the Java programming language. Its goal is to help readers learn software design by discovering the experience of the design process. To this end, the text follows a continuous narrative that introduces each element of design know-how in context, and explores alternative solutions in that context. This narrative is complemented by hundreds of code fragments and design diagrams. The first chapter is a general introduction to software design and the subsequent chapters cover design concepts and techniques. The concepts and techniques covered include interfaces, encapsulation, inheritance, design patterns, composition, functional-style design, unit testing, and many more. A major emphasis is placed on coding and experimentation as a necessary complement to reading the text. To support this aspect of the learning process, a companion website with practice exercises is provided, as well as two complete sample applications. Guidance on these sample applications is provided in "Code Exploration" insets throughout the book. Although the Java language is used as a means of conveying design-related ideas, the book's main goal is to address concepts and techniques that are applicable in a host of technologies. This second edition covers additional design techniques such as input validation and dependency injection. It also provides extended and revised treatment of many core subjects, including polymorphic copying, unit testing, the Observer pattern, and functional-style programming. This book is intended for readers who have a minimum of programming experience and want to move from writing small programs and scripts to tackling the development of larger systems. This audience naturally includes students in university-level computer science and software engineering programs. As the prerequisites to specific computing concepts are kept to a minimum, the content is also accessible to programmers with no previous background in computing. In a similar vein, understanding the code fragments requires only a minimal grasp of the Java language, such as would be taught in an introductory programming course.