Context: During requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases It is a major step taken in making crucial decisions so as to increase the economic value of a system Objective: The purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions Method: Search terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins Results: 73 Primary studies were selected from the search processes Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted to 13 Conclusion: Prioritization has been significantly discussed in the requirements engineering domain However, it was generally discovered that, existing prioritization techniques suffer from a number of limitations which includes: lack of scalability, methods of dealing with rank updates during requirements evolution, coordination among stakeholders and requirements dependency issues Also, the applicability of existing techniques in complex and real setting has not been reported yet

A systematic literature review of software requirements prioritization research

This book is an introductory text on design science, intended to support both graduate students and researchers in structuring, undertaking and presenting design science work. It builds on established design science methods as well as recent work on presenting design science studies and ethical principles for design science, and also offers novel instruments for visualizing the results, both in the form of process diagrams and through a canvas format. While the book does not presume any prior knowledge of design science, it provides readers with a thorough understanding of the subject and enables them to delve into much deeper detail, thanks to extensive sections on further reading. Design science in information systems and technology aims to create novel artifacts in the form of models, methods, and systems that support people in developing, using and maintaining IT solutions. This work focuses on design science as applied to information systems and technology, but it also includes examples from, and perspectives of, other fields of human practice. Chapter 1 provides an overview of design science and outlines its ties with empirical research. Chapter 2 discusses the various types and forms of knowledge that can be used and produced by design science research, while Chapter 3 presents a brief overview of common empirical research strategies and methods. Chapter 4 introduces a methodological framework for supporting researchers in doing design science research as well as in presenting their results. This framework includes five core activities, which are described in detail in Chapters 5 to 9. Chapter 10 discusses how to communicate design science results, while Chapter 11 compares the proposed methodological framework with methods for systems development and shows how they can be combined. Chapter 12 discusses how design science relates to research paradigms, in particular to positivism and interpretivism. Lastly, Chapter 13 discusses ethical issues and principles for design science research.

An Introduction to Design Science

Agile Requirements Engineering: A systematic literature review

In this paper, we take up the second RE17 data challenge: the identification of requirements types using the "Quality attributes (NFR)" dataset provided. We studied how accurately we can automatically classify requirements as functional (FR) and non-functional (NFR) in the dataset with supervised machine learning. Furthermore, we assessed how accurately we can identify various types of NFRs, in particular usability, security, operational, and performance requirements. We developed and evaluated a supervised machine learning approach employing meta-data, lexical, and syntactical features. We employed under-and over-sampling strategies to handle the imbalanced classes in the dataset and cross-validated the classifiers using precision, recall, and F1 metrics in a series of experiments based on the Support Vector Machine classifier algorithm. We achieve a precision and recall up to ~92% for automatically identifying FRs and NFRs. For the identification of specific NFRs, we achieve the highest precision and recall for security and performance NFRs with ~92% precision and ~90% recall. We discuss the most discriminating features of FRs and NFRs as well as the sampling strategies used with an additional dataset and their impact on the classification accuracy.

Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning

Written for those who want to develop their knowledge of requirements engineering process, whether practitioners or students.Using the latest research and driven by practical experience from industry, Requirements Engineering gives useful hints to practitioners on how to write and structure requirements. It explains the importance of Systems Engineering and the creation of effective solutions to problems. It describes the underlying representations used in system modeling and introduces the UML2, and considers the relationship between requirements and modeling. Covering a generic multi-layer requirements process, the book discusses the key elements of effective requirements management. The latest version ofDOORS (Version 7)- a software tool which serves as an enabler of a requirements management process - is also introduced to the reader here.Additional material and links are available at: http://www.requirementsengineering.info

Requirements Engineering

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Ursula Faber

Papers

Requirements Engineering A Good Practice Guide