Blockchain technology has received significant attention recently, as it offers a reliable decentralized infrastructure for all kinds of business transactions. Software-producing organizations are increasingly considering blockchain technology for inclusion into their software products. Selecting the best fitting blockchain platform requires the assessment of its functionality, adaptability, and compatibility to the existing software product. Novice software developers and architects are not experts in every domain, so they should either consult external experts or acquire knowledge themselves. The decision-making process gets more complicated as the number of decision-makers, alternatives, and criteria increases. Hence, a decision model is required to externalize and organize knowledge regarding the blockchain platform selection context. Recently, we designed a decision support system to use such decision models to support decision-makers with their technology selection problems in software production. In this article, we introduce a decision model for the blockchain platform selection problem. The decision model has been evaluated through three real-world case studies at three software-producing organizations. The case-study participants asserted that the approach provides significantly more insight into the blockchain platform selection process, provides a richer prioritized option list than if they had done their research independently, and reduces the time and cost of the decision-making process.

Requirements Engineering (RE) is a relatively young discipline, and still many advances have been achieved during the last decades. In particular, numerous RE approaches are proposed in the literature with the aim of understanding a certain problem (e.g. information systems development) and establishing a knowledge base that is shared between domain experts and developers (i.e. a requirements specification). However, there is a growing concern for empirical validations that assess RE proposals and statements. This paper is related to the assessment of the quality of functional requirements specifications, using the Method Evaluation Model (MEM) as a theoretical framework. The MEM distinguishes the actual efficacy and the perceived efficacy of a method. In order to assess the actual efficacy or RE methods, the conceptual model quality framework by Lindland et al. can be applied; in this paper, we focus on the completeness and granularity of requirements models and extend this framework by defining four new metrics (e.g. degree of functional encapsulations completeness with respect to a reference model, number of functional fragmentation errors). In order to assess the perceived efficacy, conventional questionnaires can be used. A laboratory experiment with master students has been carried out, in order to compare (using the proposed metrics) two RE methods; namely, Use Cases and Communication Analysis. With respect to actual efficacy, results indicate greater model quality (in terms of completeness and granularity) when Communication Analysis guidelines are followed. With respect to perceived efficacy, we found that Use Cases was perceived to be slightly easier to use than Communication Analysis. However, Communication Analysis was perceived to be more useful in terms of determining the proper business processes granularity. The paper discusses these results and highlights some key issues for future research in this area.

Requirements engineering (RE) is a relatively young discipline, and still many advances have been achieved during the last decades. In particular, numerous RE methods have been proposed. However, there is a growing concern for empirical validations that assess RE proposals and statements. This paper is related to the evaluation of the quality of functional requirements specifications, focusing on completeness and granularity. To do this, several concepts related to conceptual model quality are presented; these concepts lead to the definition of metrics that allow measuring certain aspects of a requirements model quality (e.g. degree of functional encapsulations completeness with respect to a reference model, number of functional fragmentation errors). A laboratory experiment with master students has been carried out, in order to compare (using the proposed metrics) two RE approaches; namely, Use Cases and Communication Analysis. Results indicate greater quality (in terms of completeness and granularity) when communication analysis guidelines are followed. Moreover, interesting issues arise from experimental results, which invite further research.