Open string field theory with stubs

Abstract There are various reasons why adding stubs to the vertices of open string field theory (OSFT) is interesting: the stubs can not only tame certain singularities and make the theory more well-behaved, but also the new theory shares a lot of similarities with closed string field theory, which helps to improve our understanding of its structure and possible solutions. In this paper we explore two natural ways of implementing stubs into the framework of OSFT, resulting in an A ∞ -algebra giving rise to infinitely many vertices. We find two distinct consistent actions, both generated by a field redefinition, interestingly sharing the same equations of motion. In the last section we illustrate their relationship and physical meaning by applying our construction to nearly marginal solutions

Using a Feature Model Configurator for Release Planning

Peer reviewe

Group Recommender Systems: An Introduction

This book presents group recommender systems, which focus on the determination of recommendations for groups of users. The authors summarize different technologies and applications of group recommender systems. They include an in-depth discussion of state-of-the-art algorithms, an overview of industrial applications, an inclusion of the aspects of decision biases in groups, and corresponding de-biasing approaches. The book includes a discussion of basic group recommendation methods, aspects of human decision making in groups, and related applications. A discussion of open research issues is included to inspire new related research. The book serves as a reference for researchers and practitioners working on group recommendation related topics

Configuring Release Plans

Release planning takes place (1) on the strategic level where the overall goal is to prioritize (high-level) requirements and (2) on the operational level where the major focus is to define more detailed implementation plans, i.e., the assignment of requirements to specific releases and often the assignment of stakeholders to requirements. In this paper, we show how release planning can be represented as a configuration task and how re-configuration tasks can be supported. Thus we advance the state-of-the-art in software release planning by introducing technologies that support the handling of inconsistencies in already existing plans.Peer reviewe

Intelligent recommendation & decision technologies for community-driven requirements engineering

Requirements Engineering (RE) represents a critical phase in the management and planning of software projects. One of the main reasons for project failure is missing or incomplete RE. In order to reduce the risk of project failure, there exists a high and urgent demand for applying intelligent technologies in RE. Since the RE process is mainly decision- and community-driven, Recommender Systems are supposed to be applied in this particular context to support stakeholders in decision-making and, hence, to increase the quality of the decisions taken by the stakeholders. This paper introduces a variety of innovative recommendation tools developed within the scope of the European Horizon 2020 research project OPENREQ. Moreover, we give an overview of user studies conducted to evaluate our approaches and present final results of selected studies. The study results indicate that the developed concepts have the potential to significantly improve the quality of requirements definition and requirements prioritization.The work presented in this paper has been conducted within the scope of the Horizon 2020 project OPENREQ (732463).Peer ReviewedPostprint (published version

Social Choice-based Explanations: An Approach to Enhancing Fairness and Consensus Aspects

Explanations are integrated into recommender systems to give users an insight into the recommendation generation process. Compared to single-user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness, which helps to take into account as much as possible group members' preferences and consensus, which persuades group members to agree on a decision. In this paper, we proposed different types of explanations and found the most effective ones in terms of increasing the fairness perception, consensus perception and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which consider the preferences of all or the majority of group members achieve the best results in terms of the mentioned dimensions. Besides, we discovered positive correlations among these aspects. In the context of repeated decisions, group members' satisfaction from previous decisions are helpful to improve the fairness perception of users concerning group recommendations and speed up the group decision-making process. Furthermore, we found out that gender diversity does influence the perception of users regarding the mentioned dimensions of the explanations. Although the proposed explanations were analyzed in group decision scenarios for non-configurable (no-attribute) items, there exist potential possibilities to apply them to explanations for configurable items