Automated decision-making techniques are useful to support engineers when performing requirements engineering tasks. However, to be effectively used in practice they need to be integrated into the organisational context, in which stakeholder engagement becomes a critical adoption factor. In this paper, we propose a tool-supported collaborative requirements prioritisation process, called GRP, which exploits gamification elements to engage distributed stakeholders to contribute to the overall decision-making process. Analytic Hierarchy Process is used as key component of the game engine, and enables an iterative prioritisation process. The GRP process has been evaluated through an exploratory case study, which has been conducted at a small software company, providing us with preliminary evidence about the effectiveness of the proposed solution. The main findings and lessons learned from the case study are presented.

Automated decision-making techniques have been proposed to support engineers in selecting and prioritising requirements. However, to be effectively used in practice they need to be integrated into the organisational context, and their users, namely the members of the development team, and more generally the project's stakeholders, need to be engaged in the resulting tool-supported decision-making process. In this demo paper, we present a tool-supported collaborative requirements prioritisation process, which exploits game elements to engage distributed stakeholders to contribute to the overall decision-making process. AHP and Genetic Algorithms are used as key component of the game engine, which enables an iterative prioritisation process. The tool is part of the tool-suite developed in the SUPERSEDE project which aims at supporting a flexible feedback-anddata-driven software evolution approach.

Gamification has been applied in software engineering contexts, and more recently in requirements engineering with the purpose of improving the motivation and engagement of people performing specific engineering tasks. But often an objective evaluation that the resulting gamified tasks successfully meet the intended goal is missing. On the other hand, current practices in designing gamified processes seem to rest on a try, test and learn approach, rather than on first principles design methods. Thus empirical evaluation should play an even more important role.We combined gamification and automated reasoning techniques to support collaborative requirements prioritization in software evolution. A first prototype has been evaluated in the context of three industrial use cases. To further investigate the impact of specific game elements, namely point-based elements, we performed a quasi-experiment comparing two versions of the tool, with and without pointsification. We present the results from these two empirical evaluations, and discuss lessons learned.

We present a novel technique to combine services, provided by mobile as well as fixed devices, on-the-fly and in a context-sensitive way within smart environments containing groups of people. The key intuition at the base of our approach is that, in those environments, similar functions are provided simultaneously by many agents on board of different devices; the issue becomes then of combining and coordinating their services in order to exploit their aggregated power or improve delivery. At the core of our work is the idea of implicit organizations, that is, teams of agents that continuously renegotiate how to provide services while the environment evolves. A concept demonstrator shows how implicit organizations can support a smart virtual meeting room. We foresee the application of our approach in assisting users and groups of users in active environments, such as an active museum. Here, our approach can enhance knowledge transfer and communication for groups visiting together cultural exhibits.

BDI (Belief, Desire, Intention) is a mature and commonly adopted architecture for intelligent agents. However, the current computational model adopted by BDI has a number of problems with concurrency control, recoverability and predictability. This has hindered the construction of agents having robust and predictable behaviour. Indeed the conceptual and practical tools needed for building dependable agent systems, resilient to faults and other unexpected situations, are still at an early stage of research. To this end, we propose to use ideas from established database technology as an adjunct to BDI agent systems. In the long term, we envisage a two layer model for distributed system development. The upper layer, called "behavioural" in our scheme, consists of concepts and techniques coming from agent research. The lower layer, called "control", is based on more traditional computer technologies and implements the actions decided by the upper layer. The emphasis of the upper behavioural layer is on flexibility of the software development process, in order to be effective in application domains that are difficult to analyse or rapidly changing. By contrast, the main focus of the lower control layer is rigorous and efficient implementation within wellknown boundaries. We discuss the development of an agent system having a computational model with well-defined correctness criteria. Instead of hardwiring robustness and fault-tolerant behaviour into agent plans, well defined notions of correctness exist at the semantic level. Verification can then be undertaken at the desired level of abstraction.