Denisse Muñante

Tool-Supported Collaborative Requirements Prioritisation

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.

Grammar Based Genetic Programming for Software Configuration Problem

Software Product Lines (SPLs) capture commonalities and variability of product families, typically represented by means of feature models. The selection of a set of suitable features when a software product is configured is typically made by exploring the space of tread-offs along different attributes of interest, for instance cost and value. In this paper, we present an approach for optimal product configuration by exploiting feature models and grammar guided genetic programming. In particular, we propose a novel encoding of candidate solutions, based on grammar representation of feature models, which ensures that relations imposed in the feature model are respected by the candidate solutions.

Gamifying Collaborative Prioritization: Does Pointsification Work?

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.

DMGame: A Gamified Collaborative Requirements Prioritisation Tool

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 projects 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.

Gathering Requirements for Software Configuration from the Crowd

Todays complex software systems consist of several components that interact in complex ways to provide services to users. In doing so, these systems go through continuous assessment of their context and configure themselves accordingly to keep user satisfaction high. A popular approach to design adaptive software systems is to perform variability modelling, for instance adopting a feature-based approach. Features describe key components and characteristics of a system, which can take different values and be combined in different ways to obtain a system behavior that can best satisfy the needs of different users, who may use the software in different contexts. These design-time models should be complemented by rules that help in deciding when to switch from one valid system configuration to a different one to fit changing user needs or preferences.Eliciting information necessary to build suitable feature models, as well as rules for dynamic reconfigurations that cover relevant scenarios is not an easy task when considering dynamic adaptation in presence of high variability in user profiles. We are experiencing this issue in a project which aims at developing dynamically personalisable software, and specifically a dynamically configurable feedback gathering tool.In this vision paper we propose to use crowdsourcing to elicit knowledge about reconfiguration requirements for dynamically adaptive systems. The proposed approach rests on a two-stage process, which involves the contribution from the crowd of potential system users, as well as from domain experts.