Abstract
To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.
This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center “On-The-Fly Computing” (CRC 901, Project Number: 160364472SFB901).
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Gottschalk, S., Yigitbas, E., Engels, G. (2020). Model-Based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs. In: Shishkov, B. (eds) Business Modeling and Software Design. BMSD 2020. Lecture Notes in Business Information Processing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-030-52306-0_18
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