No abstract

Background: Human values, such as prestige, social justice, and financial success, influence software production decision-making processes. While their subjectivity makes some values difficult to measure, their impact on software motivates our research. Aim: To contribute to the scientific understanding and the empirical investigation of human values in Software Engineering (SE). Approach: Drawing from social psychology, we consider values as mental representations to be investigated on three levels: at a system (L1), personal (L2), and instantiation level (L3). Method: We design and develop a selection of tools for the investigation of values at each level, and focus on the design, development, and use of the Values Q-Sort. Results: From our study with 12 software practitioners, it is possible to extract three values 'prototypes' indicative of an emergent typology of values considerations in SE. Conclusions: The Values Q-Sort generates quantitative values prototypes indicating values relations (L1) as well as rich personal narratives (L2) that reflect specific software practices (L3). It thus offers a systematic, empirical approach to capturing values in SE.

This article argues that human values-such as responsibility, transparency, creativity, and equality-are heavily under-represented in software engineering methods. Based on experiences with real-world projects with not-for-profits, we explore how human values can be integrated into existing participatory agile practices. We propose new ways of considering human values in software practice, including: the use of the Schwartz taxonomy of human values and values portraits to contextualise values definitions; the use of values as a way to capture the rationale for requirements to ensure a culture of values throughout the software lifecycle; and a simple adaptation of agile methods to include a role for a 'critical friend' who can champion values during decision making.

Social Software Engineering (Social SE), that is SE aiming to promote positive social change, is a rapidly emerging area. Here, software and digital artefacts are seen as tools for social change, rather than end products or 'solutions'. Moreover, Social SE requires a sustained buy-in from a range of stakeholders and end-users working in partnership with multidisciplinary software development teams often at a distance. This context poses new challenges to software engineering: it requires both an agile approach for handling uncertainties in the software development process, and the application of participatory, creative design processes to bridge the knowledge asymmetries and the geographical distances in the partnership. This paper argues for the role of design thinking in Social SE and highlights its implications for software engineering in general. It does so by reporting on the contributions that design thinkingand in particular physical design-has brought to (1) the problem space definition, (2) user requirements capture and (3) system feature design of a renewable energy forecasting system developed in partnership with a remote Scottish Island community.

There is growing research interest in exploring how biometric data is and can be shared across online social networks. However, most existing tools for sharing biometric data lock researchers into vendor-specific solutions that cannot be easily adapted to the specific researchers' requirements, users' needs and ethical considerations.To mitigate this, we investigate the requirements for open source researcher-oriented biometric data sharing systems. Requirements were captured using: first-hand insights from two prototype deployments, a systematic review of the literature, and interviews with HCI researchers who have built such tools. The requirements thus captured were implemented in the BioShare system and insights from implementing these requirements are presented. BioShare allows users both to share data but also receive inputs from remote viewers of the data in real-time. Concurrently it provides logging capabilities for researchers to analyze system interactions.