---
_id: '20795'
abstract:
- lang: eng
  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.
author:
- first_name: David
  full_name: Schmelter, David
  id: '40982'
  last_name: Schmelter
  orcid: 0000-0001-7787-5380
- first_name: Joel
  full_name: Greenyer, Joel
  last_name: Greenyer
- first_name: Jörg
  full_name: Holtmann, Jörg
  id: '3875'
  last_name: Holtmann
  orcid: 0000-0001-6141-4571
citation:
  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>'
  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>'
  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} }'
  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>.'
  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.
  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>.
  short: 'D. Schmelter, J. Greenyer, J. Holtmann, in: 4th International Workshop on
    Artificial Intelligence for Requirements Engineering (AIRE), IEEE, Lisbon, Portugal,
    2017.'
date_created: 2020-12-17T13:34:07Z
date_updated: 2022-01-06T06:54:38Z
department:
- _id: '241'
- _id: '662'
doi: 10.1109/REW.2017.14
language:
- iso: eng
place: Lisbon, Portugal
publication: 4th International Workshop on Artificial Intelligence for Requirements
  Engineering (AIRE)
publisher: IEEE
status: public
title: Toward Learning Realizable Scenario-based, Formal Requirements Specifications
type: conference
user_id: '40982'
year: '2017'
...