Requirements engineering is one of the most complex and at the same time most crucial aspects of software engineering. It typically involves different stakeholders with different backgrounds. Constant changes in both the problem and the solution domain make the work of the stakeholders extremely dynamic. New problems are discovered, additional information is needed, alternative solutions are proposed, several options are evaluated, and new hands-on experience is gained on a daily basis. The knowledge needed to define and implement requirements is immense, often interdisciplinary and constantly expanding. It typically includes engineering, management and collaboration information, as well as psychological aspects and best practices. This book discusses systematic means for managing requirements knowledge and its owners as valuable assets. It focuses on potentials and benefits of "lightweight," modern knowledge technologies such as semantic Wikis, machine learning, and recommender systems applied to requirements engineering. The 17 chapters are authored by some of the most renowned researchers in the field, distilling the discussions held over the last five years at the MARK workshop series. They present novel ideas, emerging methodologies, frameworks, tools and key industrial experience in capturing, representing, sharing, and reusing knowledge in requirements engineering. While the book primarily addresses researchers and graduate students, practitioners will also benefit from the reports and approaches presented in this comprehensive work.

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."

Requirements engineering is one of the most complex and at the same time most crucial aspects of software engineering. It typically involves different stakeholders with different backgrounds. Constant changes in both the problem and the solution domain make the work of the stakeholders extremely dynamic. New problems are discovered, additional information is needed, alternative solutions are proposed, several options are evaluated, and new hands-on experience is gained on a daily basis. The knowledge needed to define and implement requirements is immense, often interdisciplinary and constantly expanding. It typically includes engineering, management and collaboration information, as well as psychological aspects and best practices. This book discusses systematic means for managing requirements knowledge and its owners as valuable assets. It focuses on potentials and benefits of "lightweight," modern knowledge technologies such as semantic Wikis, machine learning, and recommender systems applied to requirements engineering. The 17 chapters are authored by some of the most renowned researchers in the field, distilling the discussions held over the last five years at the MARK workshop series. They present novel ideas, emerging methodologies, frameworks, tools and key industrial experience in capturing, representing, sharing, and reusing knowledge in requirements engineering. While the book primarily addresses researchers and graduate students, practitioners will also benefit from the reports and approaches presented in this comprehensive work.

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.