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Toward learning realizable scenario-based, formal requirements specifications

: Schmelter, D.; Greenyer, J.; Holtmann, J.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 25th International Requirements Engineering Conference Workshops, REW 2017. Proceedings : 4-8 September 2017, Lisbon, Portugal
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-3488-2
ISBN: 978-1-5386-3489-9
International Requirements Engineering Conference (RE) <25, 2017, Lisbon>
International Workshop on Crowd-Based Requirements Engineering (CrowdRE) <2, 2017, Lisbon>
Fraunhofer IEM ()

Distributed, software-intensive systems such as fully automated cars have to handle various situations employing message-based coordination. The growing complexity of such systems results in an increasing difficulty to achieve a high quality of the systems' requirements specifications, particularly w.r.t. the realizability of the specifications. Scenario-based requirements engineering addresses the message-based coordination of such systems and enables, if underpinned with formal languages, automatic requirements validation techniques for proving the realizability of a requirements specification. However, formal requirements modeling languages require a deep knowledge of requirements engineers and typically require many manual iterations until they find a realizable specification. In order to support requirements engineers in the stepwise development of scenario-based requirements specifications, we propose to evolve a high-quality specification from a (presumably unrealizable) manually created specification employing an evolutionary algorithm. In this paper, we show our results on automatically evolving new assumptions on the systems' environment behavior that guarantee a realizable requirements specification. Based on this contribution, we outline our research roadmap toward our long-term goal of automatically supporting requirements engineers in finding high-quality requirements specifications.