Shared understanding is essential for efficient software engineering when the risk of unsatisfactory outcome and rework of project results shall be low. Today, however, shared understanding is used mostly in an unreflected, ad-hoc way. This affects the quality of the engineered software solutions and generates re-work once the quality problems are discovered. In this article, we investigate the role, value, and usage of shared understanding in software engineering. We contribute a reflected analysis of the problem, in particular of how to rely on shared understanding that is implicit, rather than explicit. After an overview of the state of the art we discuss forms and value of shared understanding in software engineering, survey enablers and obstacles, compile existing practices for dealing with shared understanding, and present a roadmap for improving knowledge and practice in this area. Abstract Shared understanding is essential for efficient software engineering when the risk of unsatisfactory outcome and rework of project results shall be low. Today, however, shared understanding is used mostly in an unreflected, ad-hoc way. This affects the quality of the engineered software solutions and generates re-work once the quality problems are discovered. In this article, we investigate the role, value, and usage of shared understanding in software engineering. We contribute a reflected analysis of the problem, in particular of how to rely on shared understanding that is implicit, rather than explicit. After an overview of the state of the art we discuss forms and value of shared understanding in software engineering, survey enablers and obstacles, compile existing practices for dealing with shared understanding, and present a roadmap for improving knowledge and practice in this area.

No abstract

Software organizations evolve and maintain software solutions with more than a single development project. The delta specifications and artifacts that result from each project make reuse difficult and challenge a company's ability to innovate. Software product management is a growing discipline for understanding how to productize and align software with company strategy, how to evolve software, and how to coordinate product stakeholders. With product focus, in addition to project focus, planning accuracy can be improved, time-to-market reduced, product quality enhanced, and economic success sustained. This chapter provides an overview on software product management and discusses what today is known about this discipline.

No abstract

No abstract

[Context motivation] Market-oriented development involves the collaboration of many stakeholders that do not necessarily directly interact with a given development project but still influence its results. These stakeholders are part of the requirements value chain for the concerned software product.[Question/problem] Understanding the structure and functioning of requirements value chains is essential for effective stakeholder management and requirements engineering within the software product's ecosystem. [Principal ideas/results] The paper explores and exemplifies fundamental concepts that are needed to characterize and reason about requirements value chains.[Contribution] This characterization is used to describe the relevant knowledge landscape and to suggest research avenues for understanding the principles needed for managing requirements-based stakeholder collaboration. [Contribution] This characterization is used to describe the relevant knowledge landscape and to suggest research avenues for understanding the principles needed for managing requirements-based stakeholder collaboration.

No abstract

Abstract. Mobile computing and the Internet of Things promises massive amounts of data for big data analytic and machine learning. A data sharing economy is needed to make that data available for companies that wish to develop smart systems and services. While digital markets for trading data are emerging, there is no consolidated understanding of how to price data products and thus offer data vendors incentives for sharing data. This paper uses a combined keyword search and snowballing approach to systematically review the literature on the pricing of data products that are to be offered on marketplaces. The results give insights into the maturity and character of data pricing. They enable practitioners to select a pricing approach suitable for their situation and researchers to extend and mature data pricing as a topic.