[{"abstract":[{"text":"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.","lang":"eng"}],"citation":{"bibtex":"@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} }","ama":"Schmelter D, Greenyer J, Holtmann J. Toward Learning Realizable Scenario-based, Formal Requirements Specifications. In: <i>4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)</i>. Lisbon, Portugal: IEEE; 2017. doi:<a href=\"https://doi.org/10.1109/REW.2017.14\">10.1109/REW.2017.14</a>","mla":"Schmelter, David, et al. “Toward Learning Realizable Scenario-Based, Formal Requirements Specifications.” <i>4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)</i>, IEEE, 2017, doi:<a href=\"https://doi.org/10.1109/REW.2017.14\">10.1109/REW.2017.14</a>.","chicago":"Schmelter, David, Joel Greenyer, and Jörg Holtmann. “Toward Learning Realizable Scenario-Based, Formal Requirements Specifications.” In <i>4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)</i>. Lisbon, Portugal: IEEE, 2017. <a href=\"https://doi.org/10.1109/REW.2017.14\">https://doi.org/10.1109/REW.2017.14</a>.","short":"D. Schmelter, J. Greenyer, J. Holtmann, in: 4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), IEEE, Lisbon, Portugal, 2017.","ieee":"D. Schmelter, J. Greenyer, and J. Holtmann, “Toward Learning Realizable Scenario-based, Formal Requirements Specifications,” in <i>4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)</i>, 2017.","apa":"Schmelter, D., Greenyer, J., &#38; Holtmann, J. (2017). Toward Learning Realizable Scenario-based, Formal Requirements Specifications. In <i>4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)</i>. Lisbon, Portugal: IEEE. <a href=\"https://doi.org/10.1109/REW.2017.14\">https://doi.org/10.1109/REW.2017.14</a>"},"publication":"4th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)","department":[{"_id":"241"},{"_id":"662"}],"type":"conference","date_created":"2020-12-17T13:34:07Z","place":"Lisbon, Portugal","date_updated":"2022-01-06T06:54:38Z","author":[{"id":"40982","full_name":"Schmelter, David","orcid":"0000-0001-7787-5380","first_name":"David","last_name":"Schmelter"},{"full_name":"Greenyer, Joel","first_name":"Joel","last_name":"Greenyer"},{"orcid":"0000-0001-6141-4571","first_name":"Jörg","last_name":"Holtmann","full_name":"Holtmann, Jörg","id":"3875"}],"year":"2017","title":"Toward Learning Realizable Scenario-based, Formal Requirements Specifications","status":"public","user_id":"40982","doi":"10.1109/REW.2017.14","publisher":"IEEE","_id":"20795","language":[{"iso":"eng"}]}]