Feedback-aware requirements documents for smart devices

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Abstract

[Context/ Motivation] A smart device is a software-intensive system that operates autonomously and interacts to some degree with other systems over wireless connections. Such systems are often faced with uncertainty in the environment. Runtime representations of requirements have recently gained more interested to deal with this challenge and the term requirements at runtime has been established. Runtime representations of requirements support reasoning about the requirements at runtime and adapting the configuration of a system according to changes in the environment. [Questions/Problems] The research question is how the results of runtime monitoring of requirements and the system's decisions about changes in the configuration are communicated back to the requirements engineer to better understand the environment. There is a gap between the written requirements document and the dynamic requirements model inside the system. This problem is exacerbated by the fact that a requirements document are mostly informal while the dynamic requirements model is formal. [Principal ideas/results] This paper introduces an approach to bridge the gap between development time and runtime representations of requirements in order to keep them consistent and to facilitate better understanding. We propose to weave the feedback from the runtime system into requirements documents using a domain-specific language that largely retain the informal nature of requirements. An annotated requirements document helps get a better understanding of the system's actual behavior in a given environment. The approach is implemented using mbeddr, a novel set of domain-specific languages for developing embedded systems, and illustrated using a running example. © 2014 Springer International Publishing Switzerland.

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APA

Kamsties, E., Kneer, F., Voelter, M., Igel, B., & Kolb, B. (2014). Feedback-aware requirements documents for smart devices. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8396 LNCS, pp. 119–134). Springer Verlag. https://doi.org/10.1007/978-3-319-05843-6_10

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