Naming the Pain in Requirements Engineering: A Design for a Global Family of Surveys and First Results from Germany

For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations. To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice

Understanding the Impact of Business Functional Areas on the Theory of Multiple Grammar Selection

Theory of Multiple Grammar Selection (TMGS) is used to select the optimum combination of grammars when more than one grammar is needed to create conceptual models. However, as unnecessary grammars may also be selected into the optimum combination, overlap of the grammatical constructs can increase. Thus, the resulting conceptual model decreases its clarity and usefulness. One way of solving this issue is reducing the number of constructs in the reference ontology. Since ontological constructs have different importance levels in different domains the level of importance can be used as the basis for reducing number of constructs. This paper presents the result of a study we carried out to find how the importance levels of Bunge-Wand-Weber (BWW) ontology can be measured within a selected domain. As the information system domain of this study, we selected a specific business functional area namely, Sales & Distribution. Thus.the findings can be applied to any domain with similar characteristics to Sales & Distribution functional area

The importance of Quality Assurance as a Data Scientist: Commom pitfalls, examples and solutions found while validationand developing supervised binary classification models

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn today’s information era, where Data galvanizes change, companies are aiming towards competitive advantage by mining this important resource to achieve actionable insights, knowledge, and wisdom. However, to minimize bias and obtain robust long-term solutions, the methodologies that are devised from Data Science and Machine Learning approaches benefit from being carefully validated by a Quality Assurance Data Scientist, who understands not only both business rules and analytics tasks, but also understands and recommends Quality Assurance guidelines and validations. Through my experience as a Data Scientist at EDP Distribuição, I identify and systematically report on seven key Quality Assurance guidelines that helped achieve more reliable products and provided three practical examples where validation was key in discerning improvements