tBefore technology is transferred to the market, it must be validated empirically by simulating future prac-tical use of the technology. Technology prototypes are first investigated in simplified contexts, and thesesimulations are scaled up to conditions of practice step by step as more becomes known about the tech-nology. This paper discusses empirical research methods for scaling up new requirements engineering(RE) technology.When scaling up to practice, researchers want to generalize from validation studies to future practice.An analysis of scaling up technology in drug research reveals two ways to generalize, namely induc-tive generalization using statistical inference from samples, and analogic generalization using similaritybetween cases. Both are supported by abductive inference using mechanistic explanations of phenomenaobserved in the simulations. Illustrations of these inferences both in drug research and empirical REresearch are given. Next, four kinds of methods for empirical RE technology validation are given, namelyexpert opinion, single-case mechanism experiments, technical action research and statistical difference-making experiments. A series of examples from empirical RE will illustrate the use of these methods, andthe role of inductive generalization, analogic generalization, and abductive inference in them. Finally,the four kinds of empirical validation methods are compared with lists of validation methods knownfrom empirical software engineering. The lists are combined to give an overview of some of the methods,instruments and data analysis techniques that may be used in empirical RE.
- IS-Design science methodology