The IT community has always struggled with questions concerning the value of an organizationa (TM)s investment in software and hardware. It is the goal of value-based software engineering (VBSE) to develop models and measures of value which are of use for managers, developers and users as they make tradeoff decisions between, for example, quality and cost or functionality and schedule a" such decisions must be economically feasible and comprehensible to the stakeholders with differing value perspectives. VBSE has its roots in work on software engineering economics, pioneered by Barry Boehm in the early 1980s. However, the emergence of a wider scope that defines VBSE is more recent. VBSE extends the merely technical ISO software engineering definition with elements not only from economics, but also from cognitive science, finance, management science, behavioural sciences, and decision sciences, giving rise to a truly multi-disciplinary framework.

Biffl and his co-editors invited leading researchers and structured their contributions into three parts, following an introduction into the area by Boehm himself. They first detail the foundations of VBSE, followed by a presentation of state-of-the-art methods and techniques. The third part demonstrates the benefits of VBSE through concrete examples and case studies.

This book deviates from the more anecdotal style of many management-oriented software engineering books and so appeals particularly to all readers who are interested in solid foundations for high-level aspects of software engineering decision making, i.e. to product or project managers driven by economics and to software engineering researchers and students.

The IT community has always struggled with questions concerning the value of an organization's investment in software and hardware. It is the goal of value-based software engineering (VBSE) to develop models and measures of value which are of use for managers, developers and users as they make tradeoff decisions between, for example, quality and cost or functionality and schedule - such decisions must be economically feasible and comprehensible to the stakeholders with differing value perspectives. VBSE has its roots in work on software engineering economics, pioneered by Barry Boehm in the early 1980s. However, the emergence of a wider scope that defines VBSE is more recent. VBSE extends the merely technical ISO software engineering definition with elements not only from economics, but also from cognitive science, finance, management science, behavioral sciences, and decision sciences, giving rise to a truly multi-disciplinary framework. Biffl and his co-editors invited leading researchers and structured their contributions into three parts, following an introduction into the area by Boehm himself. They first detail the foundations of VBSE, followed by a presentation of state-of-the-art methods and techniques. The third part demonstrates the benefits of VBSE through concrete examples and case studies. This book deviates from the more anecdotal style of many management-oriented software engineering books and so appeals particularly to all readers who are interested in solid foundations for high-level aspects of software engineering decision making, i.e., to product or project managers driven by economics and to software engineering researchers and students.

This book constitutes the proceedings of the 23rd International Working Conference on Requirements Engineering - Foundation for Software Quality, REFSQ 2017, held in Essen, Germany, in February/March 2017. The 16 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 77 submissions. The papers were organized in topical sections named: use case models; ecosystems and innovation; human factors in requirements engineering; goal-orientation in requirements engineering; communication and collaboration; process and tool integration; visualization and representation of requirements; agile requirements engineering; natural language processing, information retrieval and machine learning traceability; quality of natural language requirements; research methodology in requirements engineering.