What If People Learn Requirements Over Time? A Rough Introduction to Requirements Economics

The overall objective of Requirements Engineering is to specify, in a systematic way, a system that satisfies the expectations of its stakeholders. Despite tremendous effort in the field, recent studies demonstrate this is objective is not always achieved. In this paper, we discuss one particularly challenging factor to Requirements Engineering projects, namely the change of requirements. We proposes a rough discussion of how learning and time explain requirements changes, how it can be introduced as a key variable in the formulation of the Requirements Engineering Problem, and how this induces costs for a requirements engineering project. This leads to a new discipline of requirements economics

Influence of Context on Decision Making during Requirements Elicitation

Requirements engineers should strive to get a better insight into decision making processes. During elicitation of requirements, decision making influences how stakeholders communicate with engineers, thereby affecting the engineers' understanding of requirements for the future information system. Empirical studies issued from Artificial Intelligence offer an adequate groundwork to understand how decision making is influenced by some particular contextual factors. However, no research has gone into the validation of such empirical studies in the process of collecting needs of the future system's users. As an answer, the paper empirically studies factors, initially identified by AI literature, that influence decision making and communication during requirements elicitation. We argue that the context's structure of the decision should be considered as a cornerstone to adequately study how stakeholders decide to communicate or not a requirement. The paper proposes a context framework to categorize former factors into specific families, and support the engineers during the elicitation process.Comment: appears in Proceedings of the 4th International Workshop on Acquisition, Representation and Reasoning with Contextualized Knowledge (ARCOE), 2012, Montpellier, France, held at the European Conference on Artificial Intelligence (ECAI-12

Towards a framework to predict start-up's business model success potential

The present master thesis regards the development a theoretical framework which is an algorithm with the goal of predicting start-ups business model’s success potential. It was designed to support entrepreneurs understanding better what venture capitalists and business angels (VC/BA) take into consideration for predicting start-ups’ success. ISC stands for Idea, Story and Context which are considered the main drivers towards start-ups’ business model success prediction by the model. This study has tested this framework on three real case-study start-ups to compare how the framework rates the three start-ups in comparison with business savvy people, such as VC/BA investors and management students. For doing this study, the framework leveraged an online platform named Business Model Composer®, an online platform for communicating business models using state-of-the-art research on business models’ topics. Finally, it is suggested how such algorithm could be integrated into an online tool to support entrepreneurs swiftly.A presente tese de mestrado centra-se no desenvolvimento de um algoritmo com o objetivo de prever o potencial de sucesso de um modelo de negócio de um start-up. O algoritmo foi concebido de forma a apoiar os empreendedores entenderem melhor o que capitalistas de risco (VC) e business angels (BA) têm em consideração para antever sucesso de start-ups. ISC significa Idea, Story e Context que são considerados pelo modelo os principais fatores conducentes à previsão de sucesso de um modelo de negócio de uma start-up. Este estudo testou essa estrutura em três casos de estudo start-ups reais, afim de comparar a forma como a framework classifica estas três start-ups vis-à-vis com pessoas experientes em negócios, como investidores VC/BA e estudantes de gestão. Para fazer este estudo, o algoritmo aproveitou uma plataforma online chamada Business Model Composer® que visa comunicar modelos de negócio e que se baseia em investigação na área de modelos de negócios. Por fim, sugere-se como poderia tal algoritmo ser integrado numa ferramenta online para ajudar empreendedores de forma clara e imediata