Toward Learning Realizable Scenario-based, Formal Requirements Specifications

conference paper Toward Learning Realizable Scenario-based, Formal Requirements Specifications David Schmelter author 409820000-0001-7787-5380 Joel Greenyer author Jörg Holtmann author 38750000-0001-6141-4571 241 department 662 department 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. IEEE2017 eng 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)10.1109/REW.2017.14 @inproceedings{Schmelter_Greenyer_Holtmann_2017, place={Lisbon, Portugal}, title={Toward Learning Realizable Scenario-based, Formal Requirements Specifications}, DOI={<a href="https://doi.org/10.1109/REW.2017.14">10.1109/REW.2017.14</a>}, booktitle={4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)}, publisher={IEEE}, author={Schmelter, David and Greenyer, Joel and Holtmann, Jörg}, year={2017} } Schmelter D, Greenyer J, Holtmann J. Toward Learning Realizable Scenario-based, Formal Requirements Specifications. In: 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). Lisbon, Portugal: IEEE; 2017. doi:<a href="https://doi.org/10.1109/REW.2017.14">10.1109/REW.2017.14</a> Schmelter, David, et al. “Toward Learning Realizable Scenario-Based, Formal Requirements Specifications.” 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), IEEE, 2017, doi:<a href="https://doi.org/10.1109/REW.2017.14">10.1109/REW.2017.14</a>. Schmelter, David, Joel Greenyer, and Jörg Holtmann. “Toward Learning Realizable Scenario-Based, Formal Requirements Specifications.” In 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). Lisbon, Portugal: IEEE, 2017. <a href="https://doi.org/10.1109/REW.2017.14">https://doi.org/10.1109/REW.2017.14</a>. D. Schmelter, J. Greenyer, J. Holtmann, in: 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), IEEE, Lisbon, Portugal, 2017. D. Schmelter, J. Greenyer, and J. Holtmann, “Toward Learning Realizable Scenario-based, Formal Requirements Specifications,” in 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), 2017. Schmelter, D., Greenyer, J., & Holtmann, J. (2017). Toward Learning Realizable Scenario-based, Formal Requirements Specifications. In 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). Lisbon, Portugal: IEEE. <a href="https://doi.org/10.1109/REW.2017.14">https://doi.org/10.1109/REW.2017.14</a> 207952020-12-17T13:34:07Z2022-01-06T06:54:38Z