Abstract
With the aim of pushing innovation through information and communication technology in the agri-business field, working closely with farmers is essential. It is especially important to systematically capture their knowledge in order to analyze, propose and design innovation artifacts (in terms of software applications). In this article, we use Scenarios to capture the knowledge of the experts that is elicited in early meetings previous to the definition of requirements. At those early stages, there are many uncertainties, and we are particularly interested in decision support. Thus, we propose an extension of the Scenarios for dealing with uncertainties. Scenarios are described in natural language, and it is very important to have an unbiased vocabulary. We complement Scenarios with a specific glossary, the Language Extended Lexicon that is also extended to decision support. According to V-model life cycle, every stage has a testing related stage. Thus, we also propose a set of rules to derive tests from the Scenarios. Summing up, we propose (i) an extension to Scenarios and the Language Extended Lexicon templates, (ii) a set of rules to derive tests, and (iii) an application to support the proposed technique. We have applied the proposed approach in a couple of case studies and we are confident that the results are promising. Nevertheless, we need to perform a further exhaustive validation.
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Acknowledgement
Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS: Enhancing and implementing knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.
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Antonelli, L. et al. (2021). An Extension to Scenarios to Deal with Business Cases for the Decision-Making Processes in the Agribusiness Domain. In: Hernández, J., Kacprzyk, J. (eds) Agriculture Value Chain - Challenges and Trends in Academia and Industry. Studies in Systems, Decision and Control, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-51047-3_3
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